From 62393f3a87738bb542b7ac1518f54ab7be21d1ec Mon Sep 17 00:00:00 2001
From: Casey <cgreene@users.noreply.github.com>
Date: Mon, 22 May 2017 13:47:17 +0000
Subject: [PATCH] Incorporated changes from my proofread of the study section
 (#493)

This build is based on
https://github.com/greenelab/deep-review/commit/c0cbf635454889f9d9702e9d2971df6206854017.

This commit was created by the following Travis CI build and job:
https://travis-ci.org/greenelab/deep-review/builds/234828498
https://travis-ci.org/greenelab/deep-review/jobs/234828499

[ci skip]

The full commit message that triggered this build is copied below:

Incorporated changes from my proofread of the study section (#493)

* initial proofreads up to metagenomics

* finish proofread

* address comments

* address build failure
---
 README.md           |   2 +-
 README.md.ots       | Bin 387 -> 387 bytes
 deep-review.pdf     | Bin 767220 -> 755643 bytes
 deep-review.pdf.ots | Bin 422 -> 422 bytes
 index.html          | 198 ++++++++++++++++++++------------------------
 index.html.ots      | Bin 422 -> 422 bytes
 6 files changed, 93 insertions(+), 107 deletions(-)

diff --git a/README.md b/README.md
index dddcc287..6b1168ef 100644
--- a/README.md
+++ b/README.md
@@ -15,4 +15,4 @@ This directory contains the following files, which are mostly ignored on the `ma
 ## Source
 
 The manuscripts in this directory were built from
-[`febdf48d7cad436df47bc75fcfff9dc0a6c01d6a`](https://github.com/greenelab/deep-review/commit/febdf48d7cad436df47bc75fcfff9dc0a6c01d6a).
+[`c0cbf635454889f9d9702e9d2971df6206854017`](https://github.com/greenelab/deep-review/commit/c0cbf635454889f9d9702e9d2971df6206854017).
diff --git a/README.md.ots b/README.md.ots
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diff --git a/deep-review.pdf b/deep-review.pdf
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@@ -78,194 +78,180 @@ <h4 id="discrimination-and-right-to-an-explanation-laws">Discrimination and &quo
 <h4 id="applications-of-deep-learning-to-longitudinal-analysis">Applications of deep learning to longitudinal analysis</h4>
 <p>Longitudinal analysis follows a population across time, for example, prospectively from birth or from the onset of particular conditions. In large patient populations, longitudinal analyses such as the Farmingham Heart Study <span class="citation">[<a href="#ref-N96QKgly">130</a>]</span> and the Avon Longitudinal Study of Parents and Children <span class="citation">[<a href="#ref-1FjSxrV1k">131</a>]</span> have yielded important insights into the development of disease and the factors contributing to health status. Yet, a common practice in EHR-based research is to take a point in time snapshot and convert patient data to a traditional vector for machine learning and statistical analysis. This results in loss of information as timing and order of events can provide insight into a patient's disease and treatment <span class="citation">[<a href="#ref-6RHepB1T">132</a>]</span>. Efforts to model sequences of events have shown promise <span class="citation">[<a href="#ref-ogs3PPp7">133</a>]</span> but require exceedingly large patient sizes due to discrete combinatorial bucketing. Lasko et al. <span class="citation">[<a href="#ref-FLX0o7bL">80</a>]</span> used autoencoders on longitudinal sequences of serum urine acid measurements to identify population subtypes. More recently, deep learning has shown promise working with both sequences (Convolutional Neural Networks) <span class="citation">[<a href="#ref-Ohd1Q9Xw">134</a>]</span> and the incorporation of past and current state (Recurrent Neural Networks, Long Short Term Memory Networks) <span class="citation">[<a href="#ref-HRXii6Ni">135</a>]</span>. This may be a particular area of opportunity for deep neural networks. The ability to discover relevant sequences of events from a large number of trajectories requires powerful and flexible feature construction methods -- an area in which deep neural networks excel.</p>
 <h2 id="deep-learning-to-study-the-fundamental-biological-processes-underlying-human-disease">Deep learning to study the fundamental biological processes underlying human disease</h2>
-<p>The study of cellular structure and core biological processes -- transcription, translation, signaling, metabolism, etc. -- in humans and model organisms will greatly impact our understanding of human disease over the long horizon <span class="citation">[<a href="#ref-ru0hjGeQ">136</a>]</span>. Predicting how cellular systems are altered by genetic variation and respond to environmental perturbations remain daunting tasks. Deep learning offers new approaches for modeling biological processes and integrating multiple types of omic data <span class="citation">[<a href="#ref-8SMDF816">137</a>]</span>, which could eventually help predict how these processes are disrupted in disease. Recent work has already advanced our ability to identify and interpret genetic variants, study microbial communities, and predict protein structures (which relates to the problems discussed in the drug development section). In addition, unsupervised deep learning has enormous potential for discovering novel cellular states from gene expression, fluorescence microscopy, and other types of data that may ultimately prove to be clinically relevant.</p>
+<p>The study of cellular structure and core biological processes -- transcription, translation, signaling, metabolism, etc. -- in humans and model organisms will greatly impact our understanding of human disease over the long horizon <span class="citation">[<a href="#ref-ru0hjGeQ">136</a>]</span>. Predicting how cellular systems respond to environmental perturbations and are altered by genetic variation remain daunting tasks. Deep learning offers new approaches for modeling biological processes and integrating multiple types of omic data <span class="citation">[<a href="#ref-8SMDF816">137</a>]</span>, which could eventually help predict how these processes are disrupted in disease. Recent work has already advanced our ability to identify and interpret genetic variants, study microbial communities, and predict protein structures, which also relates to the problems discussed in the drug development section. In addition, unsupervised deep learning has enormous potential for discovering novel cellular states from gene expression, fluorescence microscopy, and other types of data that may ultimately prove to be clinically relevant.</p>
 <p>Progress has been rapid in genomics and imaging, fields where important tasks are readily adapted to well-established deep learning paradigms. One-dimensional convolutional and recurrent neural networks are well-suited for tasks related to DNA- and RNA-binding proteins, epigenomics, and RNA splicing. Two dimensional CNNs are ideal for segmentation, feature extraction, and classification in fluorescence microscopy images <span class="citation">[<a href="#ref-MmRGFVUu">16</a>]</span>. Other areas, such as cellular signaling, are biologically important but studied less-frequently to date, with some exceptions <span class="citation">[<a href="#ref-rmjDc5rm">138</a>]</span>. This may be a consequence of data limitations or greater challenges in adapting neural network architectures to the available data. Here, we highlight several areas of investigation and assess how deep learning might move these fields forward.</p>
 <h3 id="gene-expression">Gene expression</h3>
 <p>Gene expression technologies characterize the abundance of many thousands of RNA transcripts within a given organism, tissue, or cell. This characterization can represent the underlying state of the given system and can be used to study heterogeneity across samples as well as how the system reacts to perturbation. While gene expression measurements were traditionally made by quantitative polymerase chain reaction (qPCR), low-throughput fluorescence-based methods, and microarray technologies, the field has shifted in recent years to primarily performing RNA sequencing (RNA-seq) to catalog whole transcriptomes. As RNA-seq continues to fall in price and rise in throughput, sample sizes will increase and training deep models to study gene expression will become even more useful.</p>
-<p>Already several deep learning approaches have been applied to gene expression data with varying aims. For instance, many researchers have applied unsupervised deep learning models to extract meaningful representations of gene modules or sample clusters. Denoising autoencoders have been used to cluster yeast expression microarrays into known modules representing cell cycle processes <span class="citation">[<a href="#ref-AnenJOuU">139</a>]</span> and to stratify yeast strains based on chemical and mutational perturbations <span class="citation">[<a href="#ref-yVBx9Qx4">140</a>]</span>. Shallow (one hidden layer) denoising autoencoders have also been fruitful in extracting biological insight from thousands of <em>Pseudomonas aeruginosa</em> experiments <span class="citation">[<a href="#ref-1CFhfCyWN">141</a>,<a href="#ref-zuLdSQx3">142</a>]</span> and in aggregating features relevant to specific breast cancer subtypes <span class="citation">[<a href="#ref-PBiRSdXv">24</a>]</span>. These unsupervised approaches applied to gene expression data are powerful methods for identifying gene signatures that may otherwise be overlooked. An additional benefit of unsupervised approaches is that ground truth labels, which are often difficult to acquire or are incorrect, are nonessential. However, careful interpretation must be performed regarding how the genes are aggregated into features. Precisely attributing node activations to specific biological functions risks over-interpreting models and can lead to incorrect conclusions. Also, batch effects in improperly normalized data can cause models to discover non-biological features.</p>
+<p>Already several deep learning approaches have been applied to gene expression data with varying aims. For instance, many researchers have applied unsupervised deep learning models to extract meaningful representations of gene modules or sample clusters. Denoising autoencoders have been used to cluster yeast expression microarrays into known modules representing cell cycle processes <span class="citation">[<a href="#ref-AnenJOuU">139</a>]</span> and to stratify yeast strains based on chemical and mutational perturbations <span class="citation">[<a href="#ref-yVBx9Qx4">140</a>]</span>. Shallow (one hidden layer) denoising autoencoders have also been fruitful in extracting biological insight from thousands of <em>Pseudomonas aeruginosa</em> experiments <span class="citation">[<a href="#ref-1CFhfCyWN">141</a>,<a href="#ref-zuLdSQx3">142</a>]</span> and in aggregating features relevant to specific breast cancer subtypes <span class="citation">[<a href="#ref-PBiRSdXv">24</a>]</span>. These unsupervised approaches applied to gene expression data are powerful methods for identifying gene signatures that may otherwise be overlooked. An additional benefit of unsupervised approaches is that ground truth labels, which are often difficult to acquire or are incorrect, are nonessential. However, the genes that have been aggregated into features must be interpreted carefully. Attributing each node to a single specific biological function risks over-interpreting models. Batch effects could cause models to discover non-biological features, and downstream analyses should take this into consideration.</p>
 <p>Deep learning approaches are also being applied to gene expression prediction tasks. For example, a deep neural network with three hidden layers outperformed linear regression in inferring the expression of over 20,000 target genes based on a representative, well-connected set of about 1,000 landmark genes <span class="citation">[<a href="#ref-12QQw9p7v">143</a>]</span>. However, while the deep learning model outperformed existing algorithms in nearly every scenario, the model still displayed poor performance. The paper was also limited by computational bottlenecks that required data to be split randomly into two distinct models and trained separately. It is unclear how much performance would have increased if not for computational restrictions.</p>
-<p>Alternatively, epigenetic data may have sufficient explanatory power for inference of gene expression. For instance, a convolutional neural network applied to histone modifications, termed DeepChrome, <span class="citation">[<a href="#ref-G10wkFHt">144</a>]</span> was shown to improve prediction accuracy of high or low expression over existing methods. Deep learning can also be useful for integrating different data types. For example, Liang et al. combined RBMs to integrate gene expression, DNA methylation, and miRNA data to define ovarian cancer subtypes <span class="citation">[<a href="#ref-1EtavGKI4">145</a>]</span>. While the aforementioned approaches are promising, many convert gene expression measurements to categorical or binary variables, thus ablating many complex gene expression signatures present in intermediate and relative numbers.</p>
+<p>Epigenetic data, combined with deep learning, may have sufficient explanatory power to infer gene expression. For instance, a convolutional neural network applied to histone modifications, termed DeepChrome, <span class="citation">[<a href="#ref-G10wkFHt">144</a>]</span> improved prediction accuracy of high or low gene expression over existing methods. Deep learning can also integrate different data types. For example, Liang et al. combined RBMs to integrate gene expression, DNA methylation, and miRNA data to define ovarian cancer subtypes <span class="citation">[<a href="#ref-1EtavGKI4">145</a>]</span>. While these approaches are promising, many convert gene expression measurements to categorical or binary variables, thus ablating many complex gene expression signatures present in intermediate and relative numbers.</p>
 <p>Deep learning applied to gene expression data is still in its infancy, but the future is bright. Many previously untestable hypotheses can now be interrogated as deep learning enables analysis of increasing amounts of data generated by new technologies. For example, the effects of cellular heterogeneity on basic biology and disease etiology can now be explored by single-cell RNA-seq and high-throughput fluorescence-based imaging, techniques we discuss below that will benefit immensely from deep learning approaches.</p>
 <h3 id="splicing">Splicing</h3>
-<p>Pre-mRNA transcripts can be spliced into different isoforms by retaining or skipping subsets of exons or including parts of introns, creating enormous spatiotemporal flexibility to generate multiple distinct proteins from a single gene. Unfortunately, this remarkable complexity can lend itself to defects that underlie many diseases <span class="citation">[<a href="#ref-QFK6GapR">146</a>]</span>. For instance, in Becker muscular dystrophy, a point mutation in dystrophin creates an exon splice silencer that induces skipping of exon 31. A recent study found that quantitative trait loci (QTLs) that affect splicing in lymphoblastoid cell lines are enriched within risk loci for schizophrenia, multiple sclerosis, and other immune diseases, implicating mis-splicing as a much more widespread feature of human pathologies than previously thought <span class="citation">[<a href="#ref-b6p6wxpC">147</a>]</span>.</p>
+<p>Pre-mRNA transcripts can be spliced into different isoforms by retaining or skipping subsets of exons or including parts of introns, creating enormous spatiotemporal flexibility to generate multiple distinct proteins from a single gene. This remarkable complexity can lend itself to defects that underlie many diseases <span class="citation">[<a href="#ref-QFK6GapR">146</a>]</span>. For instance, in Becker muscular dystrophy, a point mutation in dystrophin creates an exon splice silencer that induces skipping of exon 31. A recent study found that quantitative trait loci (QTLs) that affect splicing in lymphoblastoid cell lines are enriched within risk loci for schizophrenia, multiple sclerosis, and other immune diseases, implicating mis-splicing as a more widespread feature of human pathologies than previously thought <span class="citation">[<a href="#ref-b6p6wxpC">147</a>]</span>.</p>
 <p>Sequencing studies routinely return thousands of unannotated variants, but which cause functional changes in splicing and how are those changes manifested? Prediction of a &quot;splicing code&quot; has been a goal of the field for the past decade. Initial machine learning approaches used a naïve Bayes model and a 2-layer Bayesian neural network with thousands of hand-derived sequence-based features to predict the probability of exon skipping <span class="citation">[<a href="#ref-11ETDdRKr">148</a>,<a href="#ref-8VPGUHcf">149</a>]</span>. With the advent of deep learning, more complex models were built that provided better predictive accuracy <span class="citation">[<a href="#ref-17sgPdcMT">150</a>,<a href="#ref-N0HBi8MH">151</a>]</span>. Importantly, these new approaches can take in multiple kinds of epigenomic measurements as well as tissue identity and RNA binding partners of splicing factors. Deep learning is critical in furthering these kinds of integrative studies where different data types and inputs interact in unpredictable (often nonlinear) ways to create higher-order features, compared to earlier approaches that often assumed independence of features or required extensive manual fine-tuning. Moreover, as in gene expression network analysis, interrogating the hidden nodes within neural networks will likely yield new biological insights into splicing. For instance, tissue-specific splicing mechanisms could be inferred by training networks on splicing data from different tissues, then searching for common versus distinctive nodes, a technique employed by Qin et al. for tissue-specific TF binding predictions <span class="citation">[<a href="#ref-Qbtqlmhf">152</a>]</span>.</p>
-<p>A parallel effort has been to use more data with simpler models. An exhaustive study using readouts of splicing for millions of synthetic intronic sequences uncovered motifs that influence the strength of alternative splice sites <span class="citation">[<a href="#ref-mlqKTlZY">153</a>]</span>. Interestingly, they built a simple linear model using hexamer motif frequencies that successfully generalized to exon skipping. In a limited analysis using SNPs (single nucleotide polymorphisms) from three genes, it predicted exon skipping with three times the accuracy of an existing deep learning-based framework <span class="citation">[<a href="#ref-17sgPdcMT">150</a>]</span>. This case is instructive in that clever sources of data, not just more descriptive models, are still critical in yielding novel insights.</p>
-<p>We already understand how mis-splicing of a single gene can cause diseases such as Duchenne muscular dystrophy. The challenge now is to uncover how genome-wide alternative splicing underlies complex, non-Mendelian diseases such as autism, schizophrenia, Type 1 diabetes, and multiple sclerosis <span class="citation">[<a href="#ref-CNz9HwZ3">154</a>]</span>. As a proof of concept, Xiong et al. <span class="citation">[<a href="#ref-17sgPdcMT">150</a>]</span> sequenced five autism spectrum disorder and 12 control samples, each with an average of 42,000 rare variants, and identified mis-splicing in 19 genes with neural functions. Deep learning will allow scientists and clinicians to rapidly profile thousands of unannotated variants for functional effects on splicing and nominate candidates for further investigation. Moreover, these nonlinear algorithms can deconvolve the effects of multiple variants on a single splice event without the need to perform combinatorial <em>in vitro</em> experiments.</p>
-<p>The ultimate goal is to predict an individual’s tissue-specific, exon-specific splicing patterns from their genome sequence and other measurements. Knowing exactly which genes are mis-spliced in each tissue could enable a new branch of precision diagnostics that also stratifies patients and suggests targeted therapies to correct splicing defects. A continued focus on interpreting the &quot;black box&quot; of deep neural networks, along with integrating diverse data sources, will help us better understand the basic determinants of splicing and its links to complex disease, which will lead to novel diagnostics and therapeutics.</p>
+<p>A parallel effort has been to use more data with simpler models. An exhaustive study using readouts of splicing for millions of synthetic intronic sequences uncovered motifs that influence the strength of alternative splice sites <span class="citation">[<a href="#ref-mlqKTlZY">153</a>]</span>. The authors built a simple linear model using hexamer motif frequencies that successfully generalized to exon skipping. In a limited analysis using SNPs (single nucleotide polymorphisms) from three genes, it predicted exon skipping with three times the accuracy of an existing deep learning-based framework <span class="citation">[<a href="#ref-17sgPdcMT">150</a>]</span>. This case is instructive in that clever sources of data, not just more descriptive models, are still critical in yielding novel insights.</p>
+<p>We already understand how mis-splicing of a single gene can cause diseases such as Duchenne muscular dystrophy. The challenge now is to uncover how genome-wide alternative splicing underlies complex, non-Mendelian diseases such as autism, schizophrenia, Type 1 diabetes, and multiple sclerosis <span class="citation">[<a href="#ref-CNz9HwZ3">154</a>]</span>. As a proof of concept, Xiong et al. <span class="citation">[<a href="#ref-17sgPdcMT">150</a>]</span> sequenced five autism spectrum disorder and 12 control samples, each with an average of 42,000 rare variants, and identified mis-splicing in 19 genes with neural functions. Such methods may one day enable scientists and clinicians to rapidly profile thousands of unannotated variants for functional effects on splicing and nominate candidates for further investigation. Moreover, these nonlinear algorithms can deconvolve the effects of multiple variants on a single splice event without the need to perform combinatorial <em>in vitro</em> experiments. The ultimate goal is to predict an individual’s tissue-specific, exon-specific splicing patterns from their genome sequence and other measurements to enable a new branch of precision diagnostics that also stratifies patients and suggests targeted therapies to correct splicing defects. However, to achieve this we expect that methods to interpret the &quot;black box&quot; of deep neural networks and integrate diverse data sources will be required.</p>
 <h3 id="transcription-factors-and-rna-binding-proteins">Transcription factors and RNA-binding proteins</h3>
-<p>Transcription factors (TFs) and RNA-binding proteins are key components in gene regulation and higher-level biological processes. While high-throughput sequencing techniques such as chromatin immunoprecipitation and massively parallel DNA sequencing (ChIP-seq) have been able to accurately identify targets for TFs, these experiments are both time consuming and expensive. Thus, there is a need to computationally predict binding sites and understand binding specificities <em>de novo</em> from sequence data. In this section we focus on TFs, with the understanding that deep learning methods for TFs are similar to those for RNA-binding proteins, though RNA-specific models do exist <span class="citation">[<a href="#ref-qnKdqG0P">155</a>]</span>.</p>
-<p>TFs are regulatory proteins that bind to certain genomic loci and control the rate of mRNA production. ChIP-seq and related technologies are able to identify highly likely binding sites for a certain TF, and databases such as ENCODE <span class="citation">[<a href="#ref-15J98V2qM">156</a>]</span> have made freely available ChIP-seq data for hundreds of different TFs across many laboratories. In order to computationally predict transcription factor binding sites (TFBSs) on a DNA sequence, researchers initially used consensus sequences and position weight matrices to match against a test sequence <span class="citation">[<a href="#ref-ywDQIvZJ">157</a>]</span>. Simple neural network classifiers were then proposed to differentiate positive and negative binding sites but did not show significant improvements over the weight matrix matching methods <span class="citation">[<a href="#ref-uZvDdFZo">158</a>]</span>. Later, support vector machines (SVMs) outperformed the generative methods by using k-mer features <span class="citation">[<a href="#ref-JxuQvvyk">159</a>,<a href="#ref-138dgb9Ca">160</a>]</span>, but string kernel-based SVM systems are limited by their expensive computational cost, which is proportional to the number of training and testing sequences.</p>
-<p>With the advent of deep learning, Alipanahi et al. <span class="citation">[<a href="#ref-jJHZHWrl">161</a>]</span> showed that convolutional neural network models could achieve state of the art results on the TFBS task and are scalable to a large number of genomic sequences. Lanchantin et al. <span class="citation">[<a href="#ref-Dwi2eAvT">162</a>]</span> introduced several new convolutional and recurrent neural network models that further improved TFBS predictive accuracy. Due to the motif-driven nature of the TFBS task, most architectures have been convolution-based <span class="citation">[<a href="#ref-182UhQqzp">163</a>]</span>. While many models for TFBS prediction resemble computer vision and natural language processing (NLP) tasks, it is important to note that DNA sequence tasks are fundamentally different. Thus the models should be adapted from traditional deep learning models in order to account for such differences. For example, motifs may appear in either strand of a DNA sequence, resulting in two different forms of the motif (forward and reverse complement) due to complementary base pairing. To handle this issue, specialized reverse complement convolutional models share parameters to find motifs in both directions <span class="citation">[<a href="#ref-iEmvzeT8">164</a>]</span>. Because deep learning for protein binding prediction is still in the early stages, we expect to see an increase in domain-specific architectures for this task.</p>
-<p>Despite these advances, several challenges remain. First, because the inputs (ChIP-seq measurements) are continuous and most current algorithms are designed to produce binary outputs (whether or not there is TF binding at a particular site), false positives or false negatives can result depending on the threshold chosen by the algorithm. Second, most methods predict binding of TFs at sites in isolation, whereas in reality multiple TFs may compete for binding at a single site or act synergistically to co-occupy it. Fortunately, multi-task models are rapidly improving at simultaneous prediction of many TFs' binding at any given site <span class="citation">[<a href="#ref-2UI1BZuD">165</a>]</span>. Third, it is unclear exactly how to define a non-binding or &quot;negative&quot; site in the training data because the number of positive binding sites of a particular TF is relatively small with respect to the total number of base-pairs in DNA (see Discussion).</p>
-<p>While deep learning-based models can automatically extract features for TFBS prediction at the sequence level, they often cannot predict binding patterns for cell types or conditions that have not been previously studied. One solution could be to introduce a multimodal model that, in addition to sequence data, incorporates cell-line specific features such as chromatin accessibility, DNA methylation, or gene expression. Without cell-specific features, another solution could be to use domain adaptation methods where the model trains on a source cell type and uses unsupervised feature extraction methods to predict on a target cell type. TFImpute <span class="citation">[<a href="#ref-Qbtqlmhf">152</a>]</span> predicts binding in new cell type-TF pairs, but the cell types must be in the training set for other TFs besides the target TF. This is a step in the right direction, but a more general domain transfer model across cell types would be more useful.</p>
-<p>Critically, deep learning can also provide useful biological insights into TF binding. Lanchantin et al. <span class="citation">[<a href="#ref-Dwi2eAvT">162</a>]</span> and Shrikumar et al. <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>]</span> developed tools to visualize TF motifs learned from TFBS classification tasks. Alipanahi et al. <span class="citation">[<a href="#ref-jJHZHWrl">161</a>]</span> also introduced mutation maps, where they could easily mutate, add, or delete base pairs in a sequence and see how the model changed its prediction. This would be very time consuming in a lab setting but was easy to simulate using their model. As we learn to better visualize and analyze the hidden nodes within deep learning models, our understanding of TF binding motifs and dynamics will likely improve.</p>
+<p>Transcription factors (TFs) and RNA-binding proteins are key components in gene regulation and higher-level biological processes. TFs are regulatory proteins that bind to certain genomic loci and control the rate of mRNA production. While high-throughput sequencing techniques such as chromatin immunoprecipitation and massively parallel DNA sequencing (ChIP-seq) have been able to accurately identify targets for TFs, these experiments are both time consuming and expensive. Thus, there is a need to computationally predict binding sites and understand binding specificities <em>de novo</em> from sequence data. In this section we focus on TFs, with the understanding that deep learning methods for TFs are similar to those for RNA-binding proteins, though RNA-specific models do exist <span class="citation">[<a href="#ref-qnKdqG0P">155</a>]</span>.</p>
+<p>ChIP-seq and related technologies are able to identify highly likely binding sites for a certain TF, and databases such as ENCODE <span class="citation">[<a href="#ref-15J98V2qM">156</a>]</span> have made freely available ChIP-seq data for hundreds of different TFs across many laboratories. In order to computationally predict transcription factor binding sites (TFBSs) on a DNA sequence, researchers initially used consensus sequences and position weight matrices to match against a test sequence <span class="citation">[<a href="#ref-ywDQIvZJ">157</a>]</span>. Simple neural network classifiers were then proposed to differentiate positive and negative binding sites but did not show significant improvements over the weight matrix matching methods <span class="citation">[<a href="#ref-uZvDdFZo">158</a>]</span>. Later, support vector machines (SVMs) outperformed the generative methods by using k-mer features <span class="citation">[<a href="#ref-JxuQvvyk">159</a>,<a href="#ref-138dgb9Ca">160</a>]</span>, but string kernel-based SVM systems are limited by their expensive computational cost, which is proportional to the number of training and testing sequences.</p>
+<p>With the advent of deep learning, Alipanahi et al. <span class="citation">[<a href="#ref-jJHZHWrl">161</a>]</span> showed that convolutional neural network models could achieve state of the art results on the TFBS task and are scalable to a large number of genomic sequences. Lanchantin et al. <span class="citation">[<a href="#ref-Dwi2eAvT">162</a>]</span> introduced several new convolutional and recurrent neural network models that further improved TFBS predictive accuracy. Due to the motif-driven nature of the TFBS task, most architectures have been convolution-based <span class="citation">[<a href="#ref-182UhQqzp">163</a>]</span>. While many models for TFBS prediction resemble computer vision and natural language processing (NLP) tasks, it is important to note that DNA sequence tasks are fundamentally different. Thus the models should be adapted from traditional deep learning models in order to account for such differences. For example, motifs may appear in either strand of a DNA sequence, resulting in two different forms of the motif (forward and reverse complement) due to complementary base pairing. To handle this issue, specialized reverse complement convolutional models share parameters to find motifs in both directions <span class="citation">[<a href="#ref-iEmvzeT8">164</a>]</span>.</p>
+<p>Despite these advances, several challenges remain. First, because the inputs (ChIP-seq measurements) are continuous and most current algorithms are designed to produce binary outputs (whether or not there is TF binding at a particular site), false positives or false negatives can result depending on the threshold chosen by the algorithm. Second, most methods predict binding of TFs at sites in isolation, whereas in reality multiple TFs may compete for binding at a single site or act synergistically to co-occupy it. Fortunately, multi-task models are rapidly improving at simultaneous prediction of many TFs' binding at any given site <span class="citation">[<a href="#ref-2UI1BZuD">165</a>]</span>. Third, it is unclear exactly how to define a non-binding or &quot;negative&quot; site in the training data because the number of positive binding sites of a particular TF is relatively small with respect to the total number of base-pairs in a genome (see Discussion).</p>
+<p>While deep learning-based models can automatically extract features for TFBS prediction at the sequence level, they often cannot predict binding patterns for cell types or conditions that have not been previously studied. One solution could be to introduce a multimodal model that, in addition to sequence data, incorporates cell-line specific features such as chromatin accessibility, DNA methylation, or gene expression. Without cell-specific features, another solution could be to use domain adaptation methods where the model trains on a source cell type and uses unsupervised feature extraction methods to predict on a target cell type. TFImpute <span class="citation">[<a href="#ref-Qbtqlmhf">152</a>]</span> predicts binding in new cell type-TF pairs, but the cell types must be in the training set for other TFs. This is a step in the right direction, but a more general domain transfer model across cell types would be more useful.</p>
+<p>Deep learning can also provide useful biological insights into TF binding. Lanchantin et al. <span class="citation">[<a href="#ref-Dwi2eAvT">162</a>]</span> and Shrikumar et al. <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>]</span> developed tools to visualize TF motifs learned from TFBS classification tasks. Alipanahi et al. <span class="citation">[<a href="#ref-jJHZHWrl">161</a>]</span> also introduced mutation maps, where they could easily mutate, add, or delete base pairs in a sequence and see how the model changed its prediction. Though time consuming to assay in a lab, this was easy to simulate with a computational model. As we learn to better visualize and analyze the hidden nodes within deep learning models, our understanding of TF binding motifs and dynamics will likely improve.</p>
 <h3 id="promoters-enhancers-and-related-epigenomic-tasks">Promoters, enhancers, and related epigenomic tasks</h3>
-<p>Identification of promoters and other cis-regulatory elements (CREs) presents an obvious use case for deep learning. Transcriptional control is undoubtedly a vital -- and early -- part of the regulation of gene expression. An abundance of sequence and associated functional data (e.g. ENCODE <span class="citation">[<a href="#ref-15J98V2qM">156</a>]</span> and ExAC <span class="citation">[<a href="#ref-fSpvdh5l">167</a>]</span>) exists across species. At the same time, studies of gene regulation have often focused on the protein (binding) rather than the promoter level <span class="citation">[<a href="#ref-19jjiGHWc">168</a>]</span>, perhaps due to the ill-defined nature of CREs. A promoter itself can be seen as an assemblage of &quot;active&quot; binding sites for transcription factors interspersed by less-characterized and perhaps functionally silent spacer regions. However, the sequence signals that control the start and stop of transcription and translation are still not well understood, compounded by incomplete understanding of alternative transcripts and the context for these alternatives. Sequence similarity is poor even between functionally correlated genes. While homologs might be studied for insight, they may not exist or may be just as poorly characterized.</p>
-<p>Recognizing enhancers presents additional challenges. Enhancers may be up to one million base pairs upstream or downstream from the affected promoter, on either strand, even within the introns of other genes <span class="citation">[<a href="#ref-8yA3foA6">169</a>]</span>. They do not necessarily operate on the nearest gene and may affect multiple genes. Their activity is frequently tissue- or context-specific. A substantial fraction of enhancers displays modest or no conservation across species. There is no universal enhancer sequence signal or marker for enhancers, and some literature suggests that enhancers and promoters may be just categories along a spectrum <span class="citation">[<a href="#ref-J0PJHcHK">170</a>]</span>. One study <span class="citation">[<a href="#ref-5zmE7Qkb">171</a>]</span> even showed that only 33% of predicted regulatory regions could be validated, while a class of &quot;weak&quot; predicted enhancers were strong drivers of expression. Yet there is growing evidence for their vast ubiquity, making them possibly the predominant functional non-coding element. Thus, identifying enhancers is critical yet the search space is large.</p>
+<p>Transcriptional control is undoubtedly a vital, early part of the regulation of gene expression. An abundance of sequence and associated functional data (e.g. ENCODE <span class="citation">[<a href="#ref-15J98V2qM">156</a>]</span> and ExAC <span class="citation">[<a href="#ref-fSpvdh5l">167</a>]</span>) exists across species. At the same time, studies of gene regulation have often focused on the protein (binding) rather than the promoter level <span class="citation">[<a href="#ref-19jjiGHWc">168</a>]</span>, perhaps due to the ill-defined nature of cis-regulatory elements (CREs). A promoter itself can be seen as an assemblage of &quot;active&quot; binding sites for transcription factors interspersed by less-characterized and perhaps functionally silent spacer regions. However, the sequence signals that control the start and stop of transcription and translation are still not well understood, compounded by incomplete understanding of alternative transcripts and the context for these alternatives. Sequence similarity is poor even between functionally correlated genes. While homologs might be studied for insight, they may not exist or may be just as poorly characterized.</p>
+<p>Recognizing enhancers presents additional challenges. Enhancers may be up to one million base pairs upstream or downstream from the affected promoter on either strand and even within the introns of other genes <span class="citation">[<a href="#ref-8yA3foA6">169</a>]</span>. They do not necessarily operate on the nearest gene and may affect multiple genes. Their activity is frequently tissue- or context-specific. A substantial fraction of enhancers displays modest or no conservation across species. There is no universal enhancer sequence signal or marker for enhancers, and some literature suggests that enhancers and promoters may be just categories along a spectrum <span class="citation">[<a href="#ref-J0PJHcHK">170</a>]</span>. One study <span class="citation">[<a href="#ref-5zmE7Qkb">171</a>]</span> even showed that only 33% of predicted regulatory regions could be validated, while a class of &quot;weak&quot; predicted enhancers were strong drivers of expression. Yet there is growing evidence for their vast ubiquity, making them possibly the predominant functional non-coding element. Thus, identifying enhancers is critical yet the search space is large.</p>
 <p>While prior (non-deep learning) approaches have made steady improvements on promoter prediction, there is little consensus on the best approach and performance is poor. Typically algorithms will recognize only half of all promoters, with an accompanying high false positive rate <span class="citation">[<a href="#ref-huFxo9OA">172</a>]</span>. Methods with better sensitivity generally do so at the cost of poorer specificity. Conventional identification of enhancers has leaned heavily on simple conservation or laborious experimental techniques, with only moderate sensitivity and specificity. For example, while chromatin accessibility has often been used for identifying enhancers, this also &quot;recognizes&quot; a wide variety of other functional elements, like promoters, silencers, and repressors.</p>
-<p>The complex nature of CREs (and our ignorance as to their important features) is therefore a good subject for deep learning approaches. Indeed, neural networks were used for promoter recognition as early as 1996, albeit with mixed results <span class="citation">[<a href="#ref-3Ew5V1iC">173</a>]</span>. Since then, there has been much work in applying deep learning to this area, although little in the way of comparative studies or formal benchmarks. We therefore focus on a few recent important studies to outline the state of the art and extant problems.</p>
+<p>The complex nature of CREs and our lack of understanding makes them a natural candidate for deep learning approaches. Indeed, neural networks were used for promoter recognition as early as 1996, albeit with mixed results <span class="citation">[<a href="#ref-3Ew5V1iC">173</a>]</span>. Since then, there has been much work in applying deep learning to this area, although little in the way of comparative studies or formal benchmarks. We therefore focus on a few recent important studies to outline the state of the art and extant problems.</p>
 <p>Basset <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span> trained CNNs on DNA accessibility datasets, getting a marked improvement on previous methods, albeit still with a high false positive rate. The multi-task architecture resembles DeepSEA <span class="citation">[<a href="#ref-2UI1BZuD">165</a>]</span>, which predicted open chromatin regions and histone modifications in addition to TF binding. As noted above, predicting DNA accessibility conflates enhancers with other functional sites. Basset also featured a useful interpretability approach, introducing known protein binding motifs into sequences and measuring the change in predicted accessibility.</p>
 <p>Umarov et al. <span class="citation">[<a href="#ref-as2HfoSh">175</a>]</span> demonstrated the use of CNNs in recognizing promoter sequences, outperforming conventional methods (sensitivity and specificity exceeding 90%). While some results were achieved over bacterial promoters (which are considerably simpler in structure), roughly similar performance was found for human promoters. This work also included a simple method for model interpretation, randomly substituting bases in a recognized promoter region, then checking for a change in recognition (see Discussion).</p>
 <p>Xu et al. <span class="citation">[<a href="#ref-jV2YerUS">176</a>]</span> applied CNNs to the detection of enhancers, achieving incremental improvements in specificity and sensitivity over a previous SVM-based approach, and much better performance for cell-specific enhancers. A massive improvement in speed was also achieved. Additionally, they compared the performance of different CNN architectures, finding that while layers for batch normalization improved performance, deeper architectures decreased performance.</p>
 <p>Singh et al. <span class="citation">[<a href="#ref-14TqLB9iZ">177</a>]</span> approached the problem of predicting enhancer-promoter interactions from solely the sequence and location of putative enhancers and promoters in a particular cell type. Performance was comparative to state-of-the-art conventional techniques that used the whole gamut of full functional genomic and non-sequence data.</p>
 <p>Given the conflation between different CREs, the study of Li et al. <span class="citation">[<a href="#ref-1HbQQcY2q">178</a>]</span> is particularly interesting. They used a feed-forward neural network to distinguish classes of CREs and activity states. Active enhancers and promoters could be easily be distinguished, as could active and inactive elements. Perhaps unsurprisingly, it was difficult to distinguish between inactive enhancers and promoters. They also investigated the power of sequence features to drive classification, finding that beyond CpG islands, few were useful.</p>
-<p>In summary, deep learning is a promising approach for identifying CREs, able to interrogate sequence features that are complex and ill-understood, already offering marked improvements on the prior state of the art. However, the exact methodology is up for debate and needs examination and more comparative study. Work needs to be done in understanding the best architectures. The challenges in predicting TF binding -- the lack of large gold standard datasets, model interpretation, and defining negative examples -- are pertinent to CRE identification as well. Furthermore, the quality and meaning of training data needs to be closely considered, given that a &quot;promoter&quot; or &quot;enhancer&quot; may only be putative or dependent on the experimental method or context of identification. Else we risk building detectors not for CREs but putative CREs. Although most deep learning studies in this area currently focus on predicting the 1D location of enhancers, modeling 3D chromatin conformations, enhancer-promoter interactions <span class="citation">[<a href="#ref-14TqLB9iZ">177</a>]</span>, and enhancer-target gene interactions will be critical for understanding transcriptional regulation.</p>
+<p>In summary, deep learning is a promising approach for identifying CREs, able to interrogate sequence features that are complex and ill-understood, already offering marked improvements on the prior state of the art. However, neural network architectures for this task need to be systematically compared. The challenges in predicting TF binding -- such as the lack of large gold standard datasets, model interpretation, and defining negative examples -- are pertinent to CRE identification as well. Furthermore, the quality and meaning of training data needs to be closely considered, given that a &quot;promoter&quot; or &quot;enhancer&quot; may only be putative or dependent on the experimental method or context of identification. Otherwise we risk building detectors not for CREs but putative CREs. Most deep learning studies in this area currently predict the 1D location of enhancers, but modeling 3D chromatin conformations, enhancer-promoter interactions <span class="citation">[<a href="#ref-14TqLB9iZ">177</a>]</span>, and enhancer-target gene interactions will be critical for understanding transcriptional regulation.</p>
 <h3 id="micro-rna-binding">Micro-RNA binding</h3>
 <p>Prediction of microRNAs (miRNAs) in the genome as well as miRNA targets is of great interest, as they are critical components of gene regulatory networks and are often conserved across great evolutionary distance <span class="citation">[<a href="#ref-yVKIhIAf">179</a>,<a href="#ref-8lpCCppx">180</a>]</span>. While many machine learning algorithms have been applied to solve these prediction tasks, they currently require extensive feature selection and optimization. For instance, one of the most widely adopted tools for miRNA target prediction, TargetScan, trained multiple linear regression models on 14 hand-curated features including structural accessibility of the target site on the mRNA, the degree of site conservation, and predicted thermodynamic stability of the miRNA-mRNA complex <span class="citation">[<a href="#ref-12vPQi3gp">181</a>]</span>. Some of these features, including structural accessibility, are imperfect or empirically derived. In addition, current algorithms suffer from low specificity <span class="citation">[<a href="#ref-1GwC1ll6h">182</a>]</span>.</p>
-<p>As in other applications, deep learning promises to achieve equal or better performance in predictive tasks by automatically engineering complex features to minimize an objective function. Two recently published tools use different recurrent neural network-based architectures to perform miRNA and target prediction with solely sequence data as input <span class="citation">[<a href="#ref-1GwC1ll6h">182</a>,<a href="#ref-1TeyWffV">183</a>]</span>. Though the results are preliminary and still based on a validation set rather than a completely independent test set, they were able to predict microRNA target sites with 15-25% higher specificity and sensitivity than TargetScan. Excitingly, these tools seem to show that RNNs can accurately align sequences and predict bulges, mismatches, and wobble base pairing without requiring the user to input secondary structure predictions or thermodynamic calculations.</p>
-<p>Further incremental advances in deep learning for miRNA and target prediction will likely be sufficient to meet the current needs of systems biologists and other researchers, who use prediction tools mainly to nominate candidates that are then tested experimentally. Similar to other applications, the major contribution of deep learning will be to deliver deep insights into the biology of miRNA targeting as we learn to interrogate the hidden nodes within neural networks.</p>
+<p>As in other applications, deep learning promises to achieve equal or better performance in predictive tasks by automatically engineering complex features to minimize an objective function. Two recently published tools use different recurrent neural network-based architectures to perform miRNA and target prediction with solely sequence data as input <span class="citation">[<a href="#ref-1GwC1ll6h">182</a>,<a href="#ref-1TeyWffV">183</a>]</span>. Though the results are preliminary and still based on a validation set rather than a completely independent test set, they were able to predict microRNA target sites with higher specificity and sensitivity than TargetScan. Excitingly, these tools seem to show that RNNs can accurately align sequences and predict bulges, mismatches, and wobble base pairing without requiring the user to input secondary structure predictions or thermodynamic calculations.</p>
+<p>Further incremental advances in deep learning for miRNA and target prediction will likely be sufficient to meet the current needs of systems biologists and other researchers who use prediction tools mainly to nominate candidates that are then tested experimentally. Similar to other applications, the major contribution of deep learning will be to deliver deep insights into the biology of miRNA targeting as we learn to interrogate the hidden nodes within neural networks.</p>
 <h3 id="protein-secondary-and-tertiary-structure">Protein secondary and tertiary structure</h3>
-<p>Proteins play fundamental roles in almost all biological processes, and understanding their structure is critical for basic biology and drug development. UniProt currently has about 94 million protein sequences, yet fewer than 100,000 proteins across all species have experimentally-solved structures in Protein Data Bank (PDB). As a result, computational structure prediction is essential for a majority of proteins. However, this is very challenging, especially when similar solved structures (called templates) are not available in PDB. Over the past several decades, many computational methods have been developed to predict aspects of protein structure such as secondary structure, torsion angles, solvent accessibility, inter-residue contact maps, disorder regions, and side-chain packing. In recent years, various deep learning architectures have been utilized, including deep belief networks, LSTM (long short-term memory), deep convolutional neural networks, and deep convolutional neural fields (DeepCNF) <span class="citation">[<a href="#ref-UO8L6nd">29</a>,<a href="#ref-pNoAbBEu">184</a>]</span>.</p>
+<p>Proteins play fundamental roles in almost all biological processes, and understanding their structure is critical for basic biology and drug development. UniProt currently has about 94 million protein sequences, yet fewer than 100,000 proteins across all species have experimentally-solved structures in Protein Data Bank (PDB). As a result, computational structure prediction is essential for a majority of proteins. However, this is very challenging, especially when similar solved structures, called templates, are not available in PDB. Over the past several decades, many computational methods have been developed to predict aspects of protein structure such as secondary structure, torsion angles, solvent accessibility, inter-residue contact maps, disorder regions, and side-chain packing. In recent years, multiple deep learning architectures have been applied, including deep belief networks, LSTM (long short-term memory), CNNs, and deep convolutional neural fields (DeepCNF) <span class="citation">[<a href="#ref-UO8L6nd">29</a>,<a href="#ref-pNoAbBEu">184</a>]</span>.</p>
 <p>Here we focus on deep learning methods for two representative sub-problems: secondary structure prediction and contact map prediction. Secondary structure refers to local conformation of a sequence segment, while a contact map contains information on all residue-residue contacts. Secondary structure prediction is a basic problem and an almost essential module of any protein structure prediction package. Contact prediction is much more challenging than secondary structure prediction, but it has a much larger impact on tertiary structure prediction. In recent years, the accuracy of contact prediction has significantly improved <span class="citation">[<a href="#ref-BhfjKSY3">27</a>,<a href="#ref-7atXz0r">185</a>–<a href="#ref-10dNuD89l">187</a>]</span>.</p>
-<p>Protein secondary structure can exhibit three different states (alpha helix, beta strand, and loop regions) or eight finer-grained states. Q3 and Q8 accuracy pertain to 3-state or 8-state predictions, respectively. Several groups <span class="citation">[<a href="#ref-ZzaRyGuJ">28</a>,<a href="#ref-1AlhRKQbe">188</a>,<a href="#ref-UpFrhdJf">189</a>]</span> initiated the application of deep learning to protein secondary structure prediction but were unable to achieve significant improvement over the <em>de facto</em> standard method PSIPRED <span class="citation">[<a href="#ref-Aic7UyXM">190</a>]</span>, which uses two shallow feedforward neural networks. In 2014, Zhou and Troyanskaya demonstrated that they could improve Q8 accuracy by using a deep supervised and convolutional generative stochastic network <span class="citation">[<a href="#ref-8t43CQ9m">191</a>]</span>. In 2016 Wang et al. developed a DeepCNF model that significantly improved Q3 and Q8 accuracy as well as prediction of solvent accessibility and disorder regions <span class="citation">[<a href="#ref-UO8L6nd">29</a>,<a href="#ref-pNoAbBEu">184</a>]</span>. DeepCNF was the first tool to achieve Q3 accuracy of 84-85%, much higher than the 80% accuracy standard maintained by PSIPRED for more than 10 years. This improvement may be mainly due to the ability of convolutional neural fields to capture long-range sequential information, which is important for beta strand prediction. Nevertheless, improving secondary structure prediction from 80% to 84-85% is unlikely to result in a commensurate improvement in tertiary structure prediction since secondary structure mainly reflects coarse-grained local conformation of a protein structure.</p>
+<p>Protein secondary structure can exhibit three different states (alpha helix, beta strand, and loop regions) or eight finer-grained states. Q3 and Q8 accuracy pertain to 3-state or 8-state predictions, respectively. Several groups <span class="citation">[<a href="#ref-ZzaRyGuJ">28</a>,<a href="#ref-1AlhRKQbe">188</a>,<a href="#ref-UpFrhdJf">189</a>]</span> initiated the application of deep learning to protein secondary structure prediction but were unable to achieve significant improvement over the <em>de facto</em> standard method PSIPRED <span class="citation">[<a href="#ref-Aic7UyXM">190</a>]</span>, which uses two shallow feedforward neural networks. In 2014, Zhou and Troyanskaya demonstrated that they could improve Q8 accuracy by using a deep supervised and convolutional generative stochastic network <span class="citation">[<a href="#ref-8t43CQ9m">191</a>]</span>. In 2016 Wang et al. developed a DeepCNF model that significantly improved Q3 and Q8 accuracy as well as prediction of solvent accessibility and disorder regions <span class="citation">[<a href="#ref-UO8L6nd">29</a>,<a href="#ref-pNoAbBEu">184</a>]</span>. DeepCNF achieved a higher Q3 accuracy than the standard maintained by PSIPRED for more than 10 years. This improvement may be mainly due to the ability of convolutional neural fields to capture long-range sequential information, which is important for beta strand prediction. Nevertheless, the improvements in secondary structure prediction from DeepCNF are unlikely to result in a commensurate improvement in tertiary structure prediction since secondary structure mainly reflects coarse-grained local conformation of a protein structure.</p>
 <p>Protein contact prediction and contact-assisted folding (i.e. folding proteins using predicted contacts as restraints) represents a promising new direction for <em>ab initio</em> folding of proteins without good templates in PDB. Co-evolution analysis is effective for proteins with a very large number (&gt;1000) of sequence homologs <span class="citation">[<a href="#ref-10dNuD89l">187</a>]</span>, but otherwise fares poorly for proteins without many sequence homologs. By combining co-evolution information with a few other protein features, shallow neural network methods such as MetaPSICOV <span class="citation">[<a href="#ref-7atXz0r">185</a>]</span> and CoinDCA-NN <span class="citation">[<a href="#ref-kqjqFesT">192</a>]</span> have shown some advantage over pure co-evolution analysis for proteins with few sequence homologs, but their accuracy is still far from satisfactory. In recent years, deeper architectures have been explored for contact prediction, such as CMAPpro <span class="citation">[<a href="#ref-xdoT1yUx">193</a>]</span>, DNCON <span class="citation">[<a href="#ref-18bNbDNlc">194</a>]</span> and PConsC <span class="citation">[<a href="#ref-F13xtRbV">195</a>]</span>. However, blindly tested in the well-known CASP competitions, these methods did not show any advantage over MetaPSICOV <span class="citation">[<a href="#ref-7atXz0r">185</a>]</span>.</p>
 <p>Recently, Wang et al. proposed the deep learning method RaptorX-Contact <span class="citation">[<a href="#ref-BhfjKSY3">27</a>]</span>, which significantly improves contact prediction over MetaPSICOV and pure co-evolution methods, especially for proteins without many sequence homologs. It employs a network architecture formed by one 1D residual neural network and one 2D residual neural network. Blindly tested in the latest CASP competition (i.e. CASP12 <span class="citation">[<a href="#ref-zScWGveU">196</a>]</span>), RaptorX-Contact ranked first in F1 score (a widely-used performance metric combining sensitivity and specificity) on free-modeling targets as well as the whole set of targets. In CAMEO (which can be interpreted as a fully-automated CASP) <span class="citation">[<a href="#ref-u9uApoaB">197</a>]</span>, its predicted contacts were also able to fold proteins with a novel fold and only 65-330 sequence homologs. This technique also worked well on membrane proteins even when trained on non-membrane proteins <span class="citation">[<a href="#ref-39RPiE10">198</a>]</span>. RaptorX-Contact performed better mainly due to introduction of residual neural networks and exploitation of contact occurrence patterns by simultaneously predicting all the contacts in a single protein.</p>
-<p>Taken together, <em>ab initio</em> folding is becoming much easier with the advent of direct evolutionary coupling analysis and deep learning techniques. We believe it is still possible to further improve contact prediction for proteins with fewer than 1000 homologs by studying new deep network architectures. However, it is unclear whether there is an effective way to use deep learning to improve prediction for proteins with almost no sequence homologs. Finally, the deep learning methods summarized above also apply to interfacial contact prediction for protein complexes but may be less effective since on average protein complexes have fewer sequence homologs.</p>
+<p>Taken together, <em>ab initio</em> folding is becoming much easier with the advent of direct evolutionary coupling analysis and deep learning techniques. We expect further improvements in contact prediction for proteins with fewer than 1000 homologs by studying new deep network architectures. However, it is unclear if there is an effective way to use deep learning to improve prediction for proteins with few or no sequence homologs. Finally, the deep learning methods summarized above also apply to interfacial contact prediction for protein complexes but may be less effective since on average protein complexes have fewer sequence homologs.</p>
 <h3 id="morphological-phenotypes">Morphological phenotypes</h3>
-<p>A field poised for dramatic revolution by deep learning is bioimage analysis. Thus far, the primary use of deep learning for biological images has been for segmentation -- that is, for the identification of biologically relevant structures in images such as nuclei, infected cells, or vasculature -- in fluorescence or even brightfield channels <span class="citation">[<a href="#ref-40EG4ZEU">199</a>]</span>. Once so-called regions of interest have been identified, it is often straightforward to measure biological properties of interest, such as fluorescence intensities, textures, and sizes. Given the dramatic successes of deep learning in biological imaging, we simply refer to articles that review recent advancements <span class="citation">[<a href="#ref-MmRGFVUu">16</a>,<a href="#ref-40EG4ZEU">199</a>,<a href="#ref-TutLhFSz">200</a>]</span>. We believe deep learning will become commonplace for biological image segmentation once user-friendly tools exist.</p>
+<p>A field poised for dramatic revolution by deep learning is bioimage analysis. Thus far, the primary use of deep learning for biological images has been for segmentation -- that is, for the identification of biologically relevant structures in images such as nuclei, infected cells, or vasculature -- in fluorescence or even brightfield channels <span class="citation">[<a href="#ref-40EG4ZEU">199</a>]</span>. Once so-called regions of interest have been identified, it is often straightforward to measure biological properties of interest, such as fluorescence intensities, textures, and sizes. Given the dramatic successes of deep learning in biological imaging, we simply refer to articles that review recent advancements <span class="citation">[<a href="#ref-MmRGFVUu">16</a>,<a href="#ref-40EG4ZEU">199</a>,<a href="#ref-TutLhFSz">200</a>]</span>. For deep learning to become commonplace for biological image segmentation, user-friendly tools need to be developed.</p>
 <p>We anticipate an additional kind of paradigm shift in bioimaging that will be brought about by deep learning: what if images of biological samples, from simple cell cultures to three-dimensional organoids and tissue samples, could be mined for much more extensive biologically meaningful information than is currently standard? For example, a recent study demonstrated the ability to predict lineage fate in hematopoietic cells up to three generations in advance of differentiation <span class="citation">[<a href="#ref-On4vW5aU">201</a>]</span>. In biomedical research, by far the most common paradigm is for biologists to decide in advance what feature to measure in images from their assay system. Although classical methods of segmentation and feature extraction can produce hundreds of metrics per cell in an image, deep learning is unconstrained by human intuition and can in theory extract more subtle features through its hidden nodes. Already, there is evidence deep learning can surpass the efficacy of classical methods <span class="citation">[<a href="#ref-gllSeTW">202</a>]</span>, even using generic deep convolutional networks trained on natural images <span class="citation">[<a href="#ref-BMg062hc">203</a>]</span>, known as transfer learning. Recent work by Johnson et al. <span class="citation">[<a href="#ref-71c6rs2z">204</a>]</span> demonstrated how the use of a conditional adversarial autoencoder allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. The proposed model is able to generalize well to a wide range of subcellular localizations. The generative nature of the model allows it to produce high-quality synthetic images predicting localization of subcellular structures by directly modeling the localization of fluorescent labels. Notably, this approach reduces the modeling time by omitting the subcellular structure segmentation step.</p>
-<p>The impact of further improvements on biomedicine could be enormous. Comparing cell population morphologies using conventional methods of segmentation and feature extraction has already proven useful for functionally annotating genes and alleles, identifying the cellular target of small molecules, and identifying disease-specific phenotypes suitable for drug screening <span class="citation">[<a href="#ref-hkKO4QYl">205</a>–<a href="#ref-McjXFLLq">207</a>]</span>. Deep learning would bring to these new kinds of experiments -- known as image-based profiling or morphological profiling -- a higher degree of accuracy, stemming from the freedom from human-tuned feature extraction strategies. Perhaps most excitingly, focused characterization of these higher-level features will likely lead to new and valuable biological insights.</p>
+<p>The impact of further improvements on biomedicine could be enormous. Comparing cell population morphologies using conventional methods of segmentation and feature extraction has already proven useful for functionally annotating genes and alleles, identifying the cellular target of small molecules, and identifying disease-specific phenotypes suitable for drug screening <span class="citation">[<a href="#ref-hkKO4QYl">205</a>–<a href="#ref-McjXFLLq">207</a>]</span>. Deep learning would bring to these new kinds of experiments -- known as image-based profiling or morphological profiling -- a higher degree of accuracy, stemming from the freedom from human-tuned feature extraction strategies. Perhaps most excitingly, focused characterization of these higher-level features may lead to new and valuable biological insights.</p>
 <h3 id="single-cell-data">Single-cell data</h3>
-<p>Single-cell methods are generating extreme excitement as biologists recognize the vast heterogeneity within unicellular species and between cells of the same tissue type in the same organism <span class="citation">[<a href="#ref-1AWC7HsO0">208</a>]</span>. For instance, tumor cells and neurons can both harbor extensive somatic variation <span class="citation">[<a href="#ref-1GvfSy48x">209</a>]</span>. Understanding single-cell diversity in all its dimensions — genetic, epigenetic, transcriptomic, proteomic, morphologic, and metabolic — is key if precision medicine is to be targeted not only to a specific individual, but also to specific pathological subsets of cells. Single-cell methods also promise to uncover a wealth of new biological knowledge. A sufficiently large population of single cells will have enough representative &quot;snapshots&quot; to recreate timelines of dynamic biological processes. If tracking processes over time is not the limiting factor, single-cell techniques can provide maximal resolution compared to averaging across all cells in bulk tissue, enabling the study of transcriptional bursting with single-cell FISH or the heterogeneity of epigenetic patterns with single-cell Hi-C or ATAC-seq <span class="citation">[<a href="#ref-QafUwNKn">210</a>,<a href="#ref-v97iPXDw">211</a>]</span>. Joint profiling of single-cell epigenetic and transcriptional states provides unprecedented views of regulatory processes <span class="citation">[<a href="#ref-1CAw3FaPI">212</a>]</span>.</p>
+<p>Single-cell methods are generating excitement as biologists recognize the vast heterogeneity within unicellular species and between cells of the same tissue type in the same organism <span class="citation">[<a href="#ref-1AWC7HsO0">208</a>]</span>. For instance, tumor cells and neurons can both harbor extensive somatic variation <span class="citation">[<a href="#ref-1GvfSy48x">209</a>]</span>. Understanding single-cell diversity in all its dimensions -- genetic, epigenetic, transcriptomic, proteomic, morphologic, and metabolic -- is key if treatments are to be targeted not only to a specific individual, but also to specific pathological subsets of cells. Single-cell methods also promise to uncover a wealth of new biological knowledge. A sufficiently large population of single cells will have enough representative &quot;snapshots&quot; to recreate timelines of dynamic biological processes. If tracking processes over time is not the limiting factor, single-cell techniques can provide maximal resolution compared to averaging across all cells in bulk tissue, enabling the study of transcriptional bursting with single-cell fluorescence <em>in situ</em> hybridization or the heterogeneity of epigenetic patterns with single-cell Hi-C or ATAC-seq <span class="citation">[<a href="#ref-QafUwNKn">210</a>,<a href="#ref-v97iPXDw">211</a>]</span>. Joint profiling of single-cell epigenetic and transcriptional states provides unprecedented views of regulatory processes <span class="citation">[<a href="#ref-1CAw3FaPI">212</a>]</span>.</p>
 <p>However, large challenges exist in studying single cells. Relatively few cells can be assayed at once using current droplet, imaging, or microwell technologies, and low-abundance molecules or modifications may not be detected by chance due to a phenomenon known as dropout. To solve this problem, Angermueller et al. <span class="citation">[<a href="#ref-19EJTHByG">213</a>]</span> trained a neural network to predict the presence or absence of methylation of a specific CpG site in single cells based on surrounding methylation signal and underlying DNA sequence, achieving several percentage points of improvement compared to random forests or deep networks trained only on CpG or sequence information. Similar deep learning methods have been applied to impute low-resolution ChIP-seq signal from bulk tissue with great success, and they could easily be adapted to single-cell data <span class="citation">[<a href="#ref-Qbtqlmhf">152</a>,<a href="#ref-XimuXZlz">214</a>]</span>. Deep learning has also been useful for dealing with batch effects <span class="citation">[<a href="#ref-T2Md9xLY">215</a>]</span>.</p>
-<p>Examining populations of single cells can reveal biologically meaningful subsets of cells as well as their underlying gene regulatory networks <span class="citation">[<a href="#ref-1HPu3R2B4">216</a>]</span>. Unfortunately, machine learning generally struggles with imbalanced data — when there are many more examples of class 1 than class 2 — because prediction accuracy is usually evaluated over the entire dataset. To tackle this challenge, Arvaniti et al. <span class="citation">[<a href="#ref-r3Gbjksq">217</a>]</span> classified healthy and cancer cells expressing 25 markers by using the most discriminative filters from a CNN trained on the data as a linear classifier. They achieved an impressive precision of 50% to 90% with 80% recall on cells where the subset percentage ranged from 0.1 to 1%, which significantly outperformed logistic regression and distance-based outlier detection methods. However, they did not benchmark against random forests, which tend to be better with imbalanced data, and their data was fairly low dimensional. Future work will be needed to establish the utility of deep learning in cell subset identification, but the stunning improvements in image classification over the past 5 years <span class="citation">[<a href="#ref-j7KrVyi8">218</a>]</span> suggest that this goal will be achievable.</p>
-<p>The sheer quantity of &quot;omic&quot; information that can be obtained from each cell, as well as the number of cells in each dataset, uniquely position single-cell data to benefit from deep learning. In the future, lineage tracing could be revolutionized by using autoencoders to reduce the feature space of transcriptomic or variant data followed by algorithms to learn optimal cell differentiation trajectories <span class="citation">[<a href="#ref-Oljj2W96">219</a>]</span> or by feeding cell morphology and movement into neural networks <span class="citation">[<a href="#ref-On4vW5aU">201</a>]</span>. Reinforcement learning algorithms <span class="citation">[<a href="#ref-2gn6PKkv">220</a>]</span> could be trained on the evolutionary dynamics of cancer cells or bacterial cells undergoing selection pressure and reveal whether patterns of adaptation are random or deterministic, allowing us to develop therapeutic strategies that forestall resistance. It will be exciting to see the creative applications of deep learning to single-cell biology that emerge over the next few years.</p>
+<p>Examining populations of single cells can reveal biologically meaningful subsets of cells as well as their underlying gene regulatory networks <span class="citation">[<a href="#ref-1HPu3R2B4">216</a>]</span>. Unfortunately, machine learning methods generally struggle with imbalanced data -- when there are many more examples of class 1 than class 2 -- because prediction accuracy is usually evaluated over the entire dataset. To tackle this challenge, Arvaniti et al. <span class="citation">[<a href="#ref-r3Gbjksq">217</a>]</span> classified healthy and cancer cells expressing 25 markers by using the most discriminative filters from a CNN trained on the data as a linear classifier. They achieved impressive performance, even for cell types where the subset percentage ranged from 0.1 to 1%, significantly outperforming logistic regression and distance-based outlier detection methods. However, they did not benchmark against random forests, which tend to work better for imbalanced data, and their data was relatively low dimensional. Future work is needed to establish the utility of deep learning in cell subset identification, but the stunning improvements in image classification over the past 5 years <span class="citation">[<a href="#ref-j7KrVyi8">218</a>]</span> suggest transformative potential.</p>
+<p>The sheer quantity of omic information that can be obtained from each cell, as well as the number of cells in each dataset, uniquely position single-cell data to benefit from deep learning. In the future, lineage tracing could be revolutionized by using autoencoders to reduce the feature space of transcriptomic or variant data followed by algorithms to learn optimal cell differentiation trajectories <span class="citation">[<a href="#ref-Oljj2W96">219</a>]</span> or by feeding cell morphology and movement into neural networks <span class="citation">[<a href="#ref-On4vW5aU">201</a>]</span>. Reinforcement learning algorithms <span class="citation">[<a href="#ref-2gn6PKkv">220</a>]</span> could be trained on the evolutionary dynamics of cancer cells or bacterial cells undergoing selection pressure and reveal whether patterns of adaptation are random or deterministic, allowing us to develop therapeutic strategies that forestall resistance. We are excited to see the creative applications of deep learning to single-cell biology that emerge over the next few years.</p>
 <h3 id="metagenomics">Metagenomics</h3>
-<p>Metagenomics, which refers to the study of genetic material -- 16S rRNA and/or whole-genome shotgun DNA -- from microbial communities, has revolutionized the study of micro-scale ecosystems within us and around us. In recent years, machine learning has proved to be a powerful tool for metagenomic analysis. 16S rRNA has long been used to deconvolve mixtures of microbial genomes, yet this ignores &gt;99% of the genomic content. Subsequent tools aimed to classify 300-3000 base pair reads from complex mixtures of microbial genomes based on tetranucleotide frequencies (which are characteristic for different organisms <span class="citation">[<a href="#ref-N9NzkOjA">221</a>]</span>) using supervised <span class="citation">[<a href="#ref-QV551Nlx">222</a>,<a href="#ref-1HtJuEkb2">223</a>]</span> or unsupervised methods <span class="citation">[<a href="#ref-1HhqhBwrM">224</a>]</span>. Then, researchers began to use techniques that could estimate relative abundances from an entire sample, which is much faster than classifying individual reads <span class="citation">[<a href="#ref-56wEWVIl">225</a>–<a href="#ref-8DLzxOEt">228</a>]</span>. There is also great interest in identifying and annotating sequence reads <span class="citation">[<a href="#ref-qUGH5CX8">229</a>,<a href="#ref-yFOAeemA">230</a>]</span>. However, the focus on taxonomic and functional annotation is just the first step. Several groups have proposed methods to determine host or environment phenotypes from the organisms that are identified <span class="citation">[<a href="#ref-W0cYSf89">231</a>–<a href="#ref-y9s5irW">234</a>]</span> or overall sequence composition <span class="citation">[<a href="#ref-5W4KMSdT">235</a>]</span>. Also, researchers have looked into how feature selection can improve classification <span class="citation">[<a href="#ref-y9s5irW">234</a>,<a href="#ref-Kt9NojjR">236</a>]</span>, and techniques have been proposed that are classifier-independent <span class="citation">[<a href="#ref-1AN5UPfb1">237</a>,<a href="#ref-O9D66oYa">238</a>]</span>.</p>
-<p>How have neural networks been of use? Most neural networks are being used for phylogenetic classification or functional annotation from sequence data, where there is a lot of data for training. Neural networks have been applied successfully to gene annotation (e.g. Orphelia <span class="citation">[<a href="#ref-q1A2AEtO">239</a>]</span> and FragGeneScan <span class="citation">[<a href="#ref-QlbXLqH">240</a>]</span>), which usually has plenty of training examples. Representations (similar to Word2Vec <span class="citation">[<a href="#ref-1GhHIDxuW">76</a>]</span> in natural language processing) for protein family classification have been introduced and classified with a skip-gram neural network <span class="citation">[<a href="#ref-1E1PWjqTm">241</a>]</span>. Recurrent neural networks show good performance for homology and protein family identification <span class="citation">[<a href="#ref-G8RKF6sz">242</a>,<a href="#ref-81Cl5QSM">243</a>]</span>. Interestingly, Hochreiter, who invented Long Short Term Memory (LSTM), delved into homology/protein family classification in 2007, and therefore, deep learning is deeply rooted in functional classification methods.</p>
-<p>One of the first techniques of <em>de novo</em> genome binning used self-organizing maps, a type of neural network <span class="citation">[<a href="#ref-1HhqhBwrM">224</a>]</span>. Essinger et al. used Adaptive Resonance Theory (ART) to cluster similar genomic fragments and showed that it had better performance than k-means <span class="citation">[<a href="#ref-11wVLI2Hn">244</a>]</span>. However, other methods based on interpolated Markov models <span class="citation">[<a href="#ref-c4rnN1wo">245</a>]</span> have performed better than these early genome binners. Neural networks can be slow, and therefore, have had limited use for reference-based taxonomic classification, with TAC-ELM <span class="citation">[<a href="#ref-Wz7VUS03">246</a>]</span> being the only neural network-based algorithm to taxonomically classify massive amounts of metagenomic data. Also, neural networks can fail to perform if there are not enough training examples, which is the case with taxonomic classification (because only ~10% of estimated species have been sequenced). An initial study successfully applied neural networks to taxonomic classification of 16S rRNA genes, with convolutional networks providing about 10% accuracy genus-level improvement over RNNs and even random forests <span class="citation">[<a href="#ref-iPIJrVVs">247</a>]</span>. However, this study performed 10-fold cross-validation on only 3000 sequences.</p>
-<p>Neural network uses for classifying phenotype from microbial composition are just beginning. A standard multi-layer perceptron (MLP) was able to classify wound severity from microbial species present in the wound <span class="citation">[<a href="#ref-oas5tbC7">248</a>]</span>. Recently, Ditzler et al. associated soil samples with pH level using MLPs, deep-belief networks, and recursive neural networks <span class="citation">[<a href="#ref-i38A0beL">249</a>]</span>. Besides classifying the samples appropriately, they showed that internal phylogenetic tree nodes inferred by the networks were appropriate features representing low/high pH. Thus, hidden nodes might provide biological insight as well as new features for future metagenomic sample comparison. Also, an initial study has shown promise of these networks for diagnosing disease <span class="citation">[<a href="#ref-NQ5jiN7B">250</a>]</span>.</p>
-<p>Challenges remain in applying deep neural networks to metagenomics problems. They are not yet ideal for phenotype classification because most studies contain tens of samples and hundreds or thousands of features (species). Such underdetermined, or ill-conditioned, problems are still a challenge for deep neural networks that require many training examples. Also, due to convergence issues (slowness and instability due to large neural networks modeling very large datasets <span class="citation">[<a href="#ref-g2vvbB91">251</a>]</span>), taxonomic classification of reads from whole genome sequencing seems out of reach at the moment for deep neural networks. There are only thousands of full-sequenced genomes as compared to hundreds of thousands of 16S rRNA sequences available for training.</p>
-<p>However, because recurrent neural networks are showing success for denoising base calls for the Oxford Nanopore long-read sequencer <span class="citation">[<a href="#ref-1BTJ1KqRa">252</a>]</span> (discussed further in the next section), there is hope that the entire pipeline, from denoising of through functional classification, can be combined into one step by using powerful LSTMs, which have been quite successful in raw speech signal-to-meaning translation <span class="citation">[<a href="#ref-2cMhMv5A">253</a>]</span>. For example, metagenomic assembly usually requires binning then assembly, but could deep neural nets accomplish both tasks in one network? We believe the greatest potential in deep learning is to learn &quot;everything&quot; in one complex network, with a plethora of labeled (reference) data and unlabeled (microbiome experiments) examples.</p>
+<p>Metagenomics, which refers to the study of genetic material -- 16S rRNA and/or whole-genome shotgun DNA -- from microbial communities, has revolutionized the study of micro-scale ecosystems within and around us. In recent years, machine learning has proved to be a powerful tool for metagenomic analysis. 16S rRNA has long been used to deconvolve mixtures of microbial genomes, yet this ignores &gt;99% of the genomic content. Subsequent tools aimed to classify 300-3000 base pair reads from complex mixtures of microbial genomes based on tetranucleotide frequencies, which differ across organisms <span class="citation">[<a href="#ref-N9NzkOjA">221</a>]</span>, using supervised <span class="citation">[<a href="#ref-QV551Nlx">222</a>,<a href="#ref-1HtJuEkb2">223</a>]</span> or unsupervised methods <span class="citation">[<a href="#ref-1HhqhBwrM">224</a>]</span>. Then, researchers began to use techniques that could estimate relative abundances from an entire sample faster than classifying individual reads <span class="citation">[<a href="#ref-56wEWVIl">225</a>–<a href="#ref-8DLzxOEt">228</a>]</span>. There is also great interest in identifying and annotating sequence reads <span class="citation">[<a href="#ref-qUGH5CX8">229</a>,<a href="#ref-yFOAeemA">230</a>]</span>. However, the focus on taxonomic and functional annotation is just the first step. Several groups have proposed methods to determine host or environment phenotypes from the organisms that are identified <span class="citation">[<a href="#ref-W0cYSf89">231</a>–<a href="#ref-y9s5irW">234</a>]</span> or overall sequence composition <span class="citation">[<a href="#ref-5W4KMSdT">235</a>]</span>. Also, researchers have looked into how feature selection can improve classification <span class="citation">[<a href="#ref-y9s5irW">234</a>,<a href="#ref-Kt9NojjR">236</a>]</span>, and techniques have been proposed that are classifier-independent <span class="citation">[<a href="#ref-1AN5UPfb1">237</a>,<a href="#ref-O9D66oYa">238</a>]</span>.</p>
+<p>How have neural networks been of use? Most neural networks are being used for phylogenetic classification or functional annotation from sequence data where there is ample data for training. Neural networks have been applied successfully to gene annotation (e.g. Orphelia <span class="citation">[<a href="#ref-q1A2AEtO">239</a>]</span> and FragGeneScan <span class="citation">[<a href="#ref-QlbXLqH">240</a>]</span>). Representations (similar to Word2Vec <span class="citation">[<a href="#ref-1GhHIDxuW">76</a>]</span> in natural language processing) for protein family classification have been introduced and classified with a skip-gram neural network <span class="citation">[<a href="#ref-1E1PWjqTm">241</a>]</span>. Recurrent neural networks show good performance for homology and protein family identification <span class="citation">[<a href="#ref-G8RKF6sz">242</a>,<a href="#ref-81Cl5QSM">243</a>]</span>.</p>
+<p>One of the first techniques of <em>de novo</em> genome binning used self-organizing maps, a type of neural network <span class="citation">[<a href="#ref-1HhqhBwrM">224</a>]</span>. Essinger et al. <span class="citation">[<a href="#ref-11wVLI2Hn">244</a>]</span> used Adaptive Resonance Theory (ART) to cluster similar genomic fragments and showed that it had better performance than k-means. However, other methods based on interpolated Markov models <span class="citation">[<a href="#ref-c4rnN1wo">245</a>]</span> have performed better than these early genome binners. Neural networks can be slow and therefore have had limited use for reference-based taxonomic classification, with TAC-ELM <span class="citation">[<a href="#ref-Wz7VUS03">246</a>]</span> being the only neural network-based algorithm to taxonomically classify massive amounts of metagenomic data. An initial study successfully applied neural networks to taxonomic classification of 16S rRNA genes, with convolutional networks providing about 10% accuracy genus-level improvement over RNNs and random forests <span class="citation">[<a href="#ref-iPIJrVVs">247</a>]</span>. However, this study evaluated only 3000 sequences.</p>
+<p>Neural network uses for classifying phenotype from microbial composition are just beginning. A standard multi-layer perceptron (MLP) was able to classify wound severity from microbial species present in the wound <span class="citation">[<a href="#ref-oas5tbC7">248</a>]</span>. Recently, Ditzler et al. associated soil samples with pH level using MLPs, deep-belief networks (DBNs), and recurrant neural networks (RNNs) <span class="citation">[<a href="#ref-i38A0beL">249</a>]</span>. Besides classifying samples appropriately, internal phylogenetic tree nodes inferred by the networks represented features for low and high pH. Thus, hidden nodes might provide biological insight as well as new features for future metagenomic sample comparison. Also, an initial study has shown promise of these networks for diagnosing disease <span class="citation">[<a href="#ref-NQ5jiN7B">250</a>]</span>.</p>
+<p>Challenges remain in applying deep neural networks to metagenomics problems. They are not yet ideal for phenotype classification because most studies contain tens of samples and hundreds or thousands of features (species). Such underdetermined, or ill-conditioned, problems are still a challenge for deep neural networks that require many training examples. Also, due to convergence issues <span class="citation">[<a href="#ref-g2vvbB91">251</a>]</span>, taxonomic classification of reads from whole genome sequencing seems out of reach at the moment for deep neural networks. There are only thousands of full-sequenced genomes as compared to hundreds of thousands of 16S rRNA sequences available for training.</p>
+<p>However, because RNNs have been applied to base calls for the Oxford Nanopore long-read sequencer with some success <span class="citation">[<a href="#ref-1BTJ1KqRa">252</a>]</span> (discussed further in the next section), one day the entire pipeline, from denoising of through functional classification, may be combined into one step by using powerful LSTMs <span class="citation">[<a href="#ref-2cMhMv5A">253</a>]</span>. For example, metagenomic assembly usually requires binning then assembly, but could deep neural nets accomplish both tasks in one network? We believe the greatest potential in deep learning is to learn the complete characteristics of a metagenomic sample in one complex network.</p>
 <h3 id="sequencing-and-variant-calling">Sequencing and variant calling</h3>
-<p>While we have so far primarily discussed the role of deep learning in analyzing genomic data, deep learning can also substantially improve our ability to obtain the genomic data itself. We discuss two specific challenges: calling SNPs and indels (insertions and deletions) with high specificity and sensitivity and improving the accuracy of new types of data such as nanopore sequencing. These two tasks are critical for studying rare variation, allele-specific transcription and translation, and splice site mutations. In the clinical realm, sequencing of rare tumor clones and other genetic diseases will require accurate calling of SNP and indels.</p>
-<p>Current methods achieve relatively high (&gt;99%) precision at 90% recall for SNPs and indel calls from Illumina short-read data <span class="citation">[<a href="#ref-FVfZESYP">254</a>]</span>, yet this leaves a large number of potentially clinically-important remaining false positives and false negatives. These methods have so far relied on experts to build probabilistic models that reliably separate signal from noise. However, this process is time consuming and, more importantly, fundamentally limited by how well we understand and can model the factors that contribute to noise. Recently, two groups have applied deep learning to construct data-driven and, therefore, unbiased noise models. One of these models, DeepVariant, leverages Inception, a neural network trained for image classification by Google Brain, by encoding reads around a candidate SNP as a 221x100 bitmap image, where each column is a nucleotide and each row is a read from the sample library <span class="citation">[<a href="#ref-FVfZESYP">254</a>]</span>. The top 5 rows represent the reference, and the bottom 95 rows represent randomly sampled reads that overlap the candidate variant. Each RGBA (red/green/blue/alpha) image pixel encodes the base (A, C, T, G) as a different red value, quality score as a green value, strand as a blue value, and variation from the reference as the alpha value. The neural network outputs genotype probabilities for each candidate variant. They were able to achieve better performance than GATK, a leading genotype caller, even when GATK was given information about population variation for each candidate variant. Another method, still in its infancy, hand-developed 642 features for each candidate variant and fed these vectors into a fully connected deep neural network <span class="citation">[<a href="#ref-GSLRw2L5">255</a>]</span>. Unfortunately, this feature set required at least 15 iterations of software development to fine-tune, which will likely not be generalizable.</p>
-<p>Going forward, we believe that variant calling will benefit more from optimizing neural network architectures than from developing features by hand. An interesting and informative next step would be to rigorously test whether encoding raw sequence and quality data as an image, tensor, or some other mixed format produces the best variant calls. Because many of the latest neural network architectures (ResNet, Inception, Xception, and others) are already optimized for and pre-trained on generic, large-scale image datasets <span class="citation">[<a href="#ref-VMkPJjVk">256</a>]</span>, encoding genomic data as images could prove to be a generally effective, and efficient, strategy.</p>
-<p>In limited experiments, DeepVariant was robust to sequencing depth, read length, and even species <span class="citation">[<a href="#ref-FVfZESYP">254</a>]</span>. However, a model built on Illumina data, for instance, may not be optimal for PacBio long-read data or MinION nanopore data, which have vastly different specificity and sensitivity profiles and signal-to-noise characteristics. Recently, Boza et al. used bidirectional recurrent neural networks to infer the <em>E. coli</em> sequence from MinION nanopore electric current data with 2% higher per-base accuracy than the proprietary hidden Markov model-based algorithm Metrichor (86% to 88%) <span class="citation">[<a href="#ref-1BTJ1KqRa">252</a>]</span>. Unfortunately, training any neural network requires a large amount of data, which is often not available for new sequencing technologies. To circumvent this, one very preliminary study simulated mutations and spiked them into somatic and germline RNA-seq data, then trained and tested a neural network on simulated paired RNA-seq and exome sequencing data <span class="citation">[<a href="#ref-ECTm1SuA">257</a>]</span>. However, because this model was not subsequently tested on ground-truth datasets, it is unclear whether simulation can produce sufficiently realistic data to produce reliable models.</p>
+<p>While we have so far primarily discussed the role of deep learning in analyzing genomic data, deep learning can also substantially improve our ability to obtain the genomic data itself. We discuss two specific challenges: calling SNPs and indels (insertions and deletions) with high specificity and sensitivity and improving the accuracy of new types of data such as nanopore sequencing. These two tasks are critical for studying rare variation, allele-specific transcription and translation, and splice site mutations. In the clinical realm, sequencing of rare tumor clones and other genetic diseases will require accurate calling of SNPs and indels.</p>
+<p>Current methods achieve relatively high (&gt;99%) precision at 90% recall for SNPs and indel calls from Illumina short-read data <span class="citation">[<a href="#ref-FVfZESYP">254</a>]</span>, yet this leaves a large number of potentially clinically-important remaining false positives and false negatives. These methods have so far relied on experts to build probabilistic models that reliably separate signal from noise. However, this process is time consuming and fundamentally limited by how well we understand and can model the factors that contribute to noise. Recently, two groups have applied deep learning to construct data-driven unbiased noise models. One of these models, DeepVariant, leverages Inception, a neural network trained for image classification by Google Brain, by encoding reads around a candidate SNP as a 221x100 bitmap image, where each column is a nucleotide and each row is a read from the sample library <span class="citation">[<a href="#ref-FVfZESYP">254</a>]</span>. The top 5 rows represent the reference, and the bottom 95 rows represent randomly sampled reads that overlap the candidate variant. Each RGBA (red/green/blue/alpha) image pixel encodes the base (A, C, T, G) as a different red value, quality score as a green value, strand as a blue value, and variation from the reference as the alpha value. The neural network outputs genotype probabilities for each candidate variant. They were able to achieve better performance than GATK, a leading genotype caller, even when GATK was given information about population variation for each candidate variant. Another method, still in its infancy, hand-developed 642 features for each candidate variant and fed these vectors into a fully connected deep neural network <span class="citation">[<a href="#ref-GSLRw2L5">255</a>]</span>. Unfortunately, this feature set required at least 15 iterations of software development to fine-tune, which suggests that these models may not generalize.</p>
+<p>Going forward, variant calling will benefit more from optimizing neural network architectures than from developing features by hand. An interesting and informative next step would be to rigorously test if encoding raw sequence and quality data as an image, tensor, or some other mixed format produces the best variant calls. Because many of the latest neural network architectures (ResNet, Inception, Xception, and others) are already optimized for and pre-trained on generic, large-scale image datasets <span class="citation">[<a href="#ref-VMkPJjVk">256</a>]</span>, encoding genomic data as images could prove to be a generally effective and efficient strategy.</p>
+<p>In limited experiments, DeepVariant was robust to sequencing depth, read length, and even species <span class="citation">[<a href="#ref-FVfZESYP">254</a>]</span>. However, a model built on Illumina data, for instance, may not be optimal for PacBio long-read data or MinION nanopore data, which have vastly different specificity and sensitivity profiles and signal-to-noise characteristics. Recently, Boza et al. used bidirectional recurrent neural networks to infer the <em>E. coli</em> sequence from MinION nanopore electric current data with higher per-base accuracy than the proprietary hidden Markov model-based algorithm Metrichor <span class="citation">[<a href="#ref-1BTJ1KqRa">252</a>]</span>. Unfortunately, training any neural network requires a large amount of data, which is often not available for new sequencing technologies. To circumvent this, one very preliminary study simulated mutations and spiked them into somatic and germline RNA-seq data, then trained and tested a neural network on simulated paired RNA-seq and exome sequencing data <span class="citation">[<a href="#ref-ECTm1SuA">257</a>]</span>. However, because this model was not subsequently tested on ground-truth datasets, it is unclear whether simulation can produce sufficiently realistic data to produce reliable models.</p>
 <p>Method development for interpreting new types of sequencing data has historically taken two steps: first, easily implemented hard cutoffs that prioritize specificity over sensitivity, then expert development of probabilistic models with hand-developed inputs <span class="citation">[<a href="#ref-ECTm1SuA">257</a>]</span>. We anticipate that these steps will be replaced by deep learning, which will infer features simply by its ability to optimize a complex model against data.</p>
 <h2 id="the-impact-of-deep-learning-in-treating-disease-and-developing-new-treatments">The impact of deep learning in treating disease and developing new treatments</h2>
-<p>Given the ever-present need to make better, faster interventions at the point of care -- incorporating the complex calculus of a patients symptoms, diagnostics, and life history -- there have been many attempts to apply deep learning to patient treatment. Success in this area would be very useful for personalized healthcare or precision medicine <span class="citation">[<a href="#ref-VOQtVhWs">258</a>,<a href="#ref-3JyJ3DTh">259</a>]</span>. Earlier, we presented possibilities for patient categorization. Here, we examine the potential for better treatment, which broadly, may divided into methods for improved choices of interventions for patients and those for development of new interventions.</p>
-<h3 id="categorizing-patients-for-clinical-decision-making">Categorizing patients for clinical decision making</h3>
-<p>As long ago as 1996, Tu <span class="citation">[<a href="#ref-kL0B4m9d">260</a>]</span> compared the effectiveness of artificial neural networks and logistic regression, questioning whether these techniques would replace traditional statistical methods for predicting medical outcomes such as myocardial infarction <span class="citation">[<a href="#ref-jdg2u7bX">261</a>]</span> or mortality <span class="citation">[<a href="#ref-xX68eyvs">262</a>]</span>. He posited that while neural networks have several advantages in representational power, the difficulties in interpretation may limit clinical applications, a limitation that still remains today. Similarly, in 2006 Lisboa and Taktak <span class="citation">[<a href="#ref-qxxwkSAT">263</a>]</span> examined the use of artificial neural networks in medical journals, concluding that they improved healthcare relative to traditional screening methods in 21 of 27 studies.</p>
+<p>Given the need to make better, faster interventions at the point of care -- incorporating the complex calculus of a patients symptoms, diagnostics, and life history -- there have been many attempts to apply deep learning to patient treatment. Success in this area could help to enable personalized healthcare or precision medicine <span class="citation">[<a href="#ref-VOQtVhWs">258</a>,<a href="#ref-3JyJ3DTh">259</a>]</span>. Earlier, we reviewed approaches for patient categorization. Here, we examine the potential for better treatment, which broadly, may divided into methods for improved choices of interventions for patients and those for development of new interventions.</p>
+<h3 id="clinical-decision-making">Clinical decision making</h3>
+<p>In 1996, Tu <span class="citation">[<a href="#ref-kL0B4m9d">260</a>]</span> compared the effectiveness of artificial neural networks and logistic regression, questioning whether these techniques would replace traditional statistical methods for predicting medical outcomes such as myocardial infarction <span class="citation">[<a href="#ref-jdg2u7bX">261</a>]</span> or mortality <span class="citation">[<a href="#ref-xX68eyvs">262</a>]</span>. He posited that while neural networks have several advantages in representational power, the difficulties in interpretation may limit clinical applications, a limitation that still remains today. In addition, the challenges faced by physicians parallel those encountered by deep learning. For a given patient, the number of possible diseases is very large, with a long tail of rare diseases and patients are highly heterogeneous and may present with very different signs and symptoms for the same disease. Still, in 2006 Lisboa and Taktak <span class="citation">[<a href="#ref-qxxwkSAT">263</a>]</span> examined the use of artificial neural networks in medical journals, concluding that they improved healthcare relative to traditional screening methods in 21 of 27 studies.</p>
 <p>While further progress has been made in using deep learning for clinical decision making, it is hindered by a challenge common to many deep learning applications: it is much easier to predict an outcome than to suggest an action to change the outcome. Several attempts <span class="citation">[<a href="#ref-qXdO2aMm">84</a>,<a href="#ref-1FE0F2pQ">86</a>]</span> at recasting the clinical decision-making problem into a prediction problem (i.e. prediction of which treatment will most improve the patient's health) have accurately predicted survival patterns, but technical and medical challenges remain for clinical adoption (similar to those for categorization). In particular, remaining barriers include actionable interpretability of deep learning models, fitting deep models to limited and heterogeneous data, and integrating complex predictive models into a dynamic clinical environment.</p>
-<p>A critical challenge in moving from prediction to treatment recommendations is the necessity to establish a causal relationship for a recommendation. Causal inference is often framed in terms of counterfactual question <span class="citation">[<a href="#ref-cpNVdlL7">264</a>]</span>. Johansson et al. <span class="citation">[<a href="#ref-173ftiSzF">265</a>]</span> use deep neural networks to create representation models for covariates that capture nonlinear effects and show significant performance improvements over existing models. In a less formal approach, Kale et al. <span class="citation">[<a href="#ref-FUIfIdE">266</a>]</span> first create a deep neural network to model clinical time series and then analyze the relationship of the hidden features to the output using a causal approach.</p>
-<h4 id="applications">Applications</h4>
-<h5 id="trajectory-prediction-for-treatment">Trajectory prediction for treatment</h5>
-<p>A common application for deep learning techniques in this domain is to leverage the temporal structure of healthcare records. As previously discussed, many studies <span class="citation">[<a href="#ref-4zpZxjHR">267</a>–<a href="#ref-glyI7H6F">270</a>]</span> have used deep recurrent networks to categorize patients, but most stop short of suggesting clinical decisions. Nemati et al. <span class="citation">[<a href="#ref-16OQvsRqJ">271</a>]</span> used deep reinforcement learning to optimize a heparin dosing policy for intensive care patients. However, because the ideal dosing policy is unknown, the model's predictions must be evaluated on counter-factual data. This represents a common challenge when bridging the gap between research and clinical practice. Because the ground-truth is unknown, researchers struggle to evaluate model predictions in the absence of interventional data, but clinical application is unlikely until the model has been shown to be effective. The impressive applications of deep reinforcement learning to other domains <span class="citation">[<a href="#ref-2gn6PKkv">220</a>]</span> have relied on knowledge of the underlying processes (e.g. the rules of the game). Some models have been developed for targeted medical problems <span class="citation">[<a href="#ref-eCrLGgiX">272</a>]</span>, but a generalized engine is beyond current capabilities. Further development of the rules underlying biological processes could unleash deep learning methods that are currently hampered by the difficulties of counter-factual inference.</p>
-<h5 id="efficient-clinical-trials">Efficient clinical trials</h5>
+<p>A critical challenge in providing treatment recommendations is identifying a causal relationship for each recommendation. Causal inference is often framed in terms of counterfactual question <span class="citation">[<a href="#ref-cpNVdlL7">264</a>]</span>. Johansson et al. <span class="citation">[<a href="#ref-173ftiSzF">265</a>]</span> use deep neural networks to create representation models for covariates that capture nonlinear effects and show significant performance improvements over existing models. In a less formal approach, Kale et al. <span class="citation">[<a href="#ref-FUIfIdE">266</a>]</span> first create a deep neural network to model clinical time series and then analyze the relationship of the hidden features to the output using a causal approach.</p>
+<p>A common challenge for deep learning is the interpretability of the models and their predictions. The task of clinical decision making is necessarily risk-averse, so model interpretability is key. Without clear reasoning, it is difficult to establish trust in a model. As described above, there has been some work to directly assign treatment plans without interpretability; however, the removal of human experts from the decision-making loop make the models difficult to integrate with clinical practice. To alleviate this challenge, several studies have attempted to create more interpretable deep models, either specifically for healthcare or as a general procedure for deep learning (see Discussion).</p>
+<h4 id="predicting-patient-trajectories">Predicting patient trajectories</h4>
+<p>A common application for deep learning in this domain is the temporal structure of healthcare records. Many studies <span class="citation">[<a href="#ref-4zpZxjHR">267</a>–<a href="#ref-glyI7H6F">270</a>]</span> have used deep recurrent networks to categorize patients, but most stop short of suggesting clinical decisions. Nemati et al. <span class="citation">[<a href="#ref-16OQvsRqJ">271</a>]</span> used deep reinforcement learning to optimize a heparin dosing policy for intensive care patients. However, because the ideal dosing policy is unknown, the model's predictions must be evaluated on counter-factual data. This represents a common challenge when bridging the gap between research and clinical practice. Because the ground-truth is unknown, researchers struggle to evaluate model predictions in the absence of interventional data, but clinical application is unlikely until the model has been shown to be effective. The impressive applications of deep reinforcement learning to other domains <span class="citation">[<a href="#ref-2gn6PKkv">220</a>]</span> have relied on knowledge of the underlying processes (e.g. the rules of the game). Some models have been developed for targeted medical problems <span class="citation">[<a href="#ref-eCrLGgiX">272</a>]</span>, but a generalized engine is beyond current capabilities.</p>
+<h4 id="clinical-trials-efficiency">Clinical trials efficiency</h4>
 <p>A clinical deep learning task that has been more successful is the assignment of patients to clinical trials. Ithapu et al. <span class="citation">[<a href="#ref-eehGXQlY">273</a>]</span> used a randomized denoising autoencoder to learn a multimodal imaging marker that predicts future cognitive and neural decline from positron emission tomography (PET), amyloid florbetapir PET, and structural magnetic resonance imaging. By accurately predicting which cases will progress to dementia, they were able to efficiently assign patients to a clinical trial and reduced the required sample sizes by a factor of five. Similarly, Artemov et al. <span class="citation">[<a href="#ref-mo3GQwJj">274</a>]</span> applied deep learning to predict which clinical trials were likely to fail and which were likely to succeed. By predicting the side effects and pathway activations of each drug and translating these activations to a success probability, their deep learning-based approach was able to significantly outperform a random forest classifier trained on gene expression changes. These approaches suggest promising directions to improve the efficiency of clinical trials and accelerate drug development.</p>
-<h4 id="challenges">Challenges</h4>
-<h5 id="actionable-interpretability">Actionable interpretability</h5>
-<p>A common challenge in many applied deep learning problems is the consideration of deep learning models as uninterpretable &quot;black boxes&quot;. Without human-intelligible reasoning for the model's predictions, it is difficult to trust the model. This presents a major challenge for the risk-averse task of clinical decision making. As described above, there has been some work to directly assign treatment plans without interpretability; however, the removal of human experts from the decision-making loop make the models difficult to integrate with clinical practice. To alleviate this challenge, several studies have attempted to create more interpretable deep models, either specifically for healthcare or as a general procedure for deep learning (see Discussion). Further work in interpreting predictions and understanding the knowledge learned by deep neural networks seem necessary for transformative impact in clinical practice.</p>
-<h5 id="integrating-deep-learning-with-clinical-practice">Integrating deep learning with clinical practice</h5>
-<p>As deep learning models are difficult to interpret, many current models have been designed to replace aspects of clinical practice rather than to assist trained clinicians. This makes it difficult to integrate deep learning with clinical decision making. In addition, the challenges that physicians face are largely similar to those faced by machine learning models. For a given patient, the number of possible diseases is very large, with a long tail of rare diseases. Furthermore, patients are highly heterogeneous and may present with very different signs and symptoms for the same disease. Physicians are experienced in treating patients with common diseases, but rare diseases are extremely challenging. Unfortunately, machine learning methods also struggle for rare diseases. Directly applying current deep learning models to diagnose patients with rare diseases would require prohibitively large datasets to provide sufficient training data to capture the rare instances. Focused effort in reducing the data requirements of deep learning by integrating pre-existing knowledge or compiling large datasets of patient records may unlock the power of deep learning for clinical practice.</p>
 <h3 id="drug-repositioning">Drug repositioning</h3>
 <p>Drug repositioning (or repurposing) is an attractive option for delivering new drugs to the market because of the high costs and failure rates associated with more traditional drug discovery approaches <span class="citation">[<a href="#ref-13c9OPizf">275</a>,<a href="#ref-79Ktl2">276</a>]</span>. A decade ago, the concept of the Connectivity Map <span class="citation">[<a href="#ref-Ot5bUkmI">277</a>]</span> had a sizeable impact on the field. Reverse matching disease gene expression signatures with a large set of reference compound profiles allowed researchers to formulate repurposing hypotheses at scale using a simple non-parametric test. Since then, several advanced computational methods have been applied to formulate and validate drug repositioning hypotheses <span class="citation">[<a href="#ref-gTwjIQqB">278</a>–<a href="#ref-ir7ElHha">280</a>]</span>. Using supervised learning and collaborative filtering to tackle this type of problem is proving successful, especially when coupling disease or compound omic data with topological information from protein-protein or protein-compound interaction networks <span class="citation">[<a href="#ref-M1EW8Rfl">281</a>–<a href="#ref-18lqFDKRR">283</a>]</span>.</p>
-<p>For example, Menden et al. <span class="citation">[<a href="#ref-QcwZC8wG">284</a>]</span> used a shallow neural network to predict sensitivity of cancer cell lines to drug treatment using both cell line and drug features, opening the door to precision medicine and drug repositioning opportunities in cancer. More recently, Aliper et al <span class="citation">[<a href="#ref-EMDwvRGb">35</a>]</span> used gene- and pathway-level drug perturbation transcriptional profiles from the Library of Network-Based Cellular Signatures (LINCS) <span class="citation">[<a href="#ref-ppGS5h4v">285</a>]</span> to train a fully connected deep neural network to predict drug therapeutic uses and indications. By using confusion matrices and leveraging misclassification, the authors formulated a number of interesting hypotheses, including repurposing cardiovascular drugs such as otenzepad and pinacidil for neurological disorders.</p>
-<p>Drug repositioning can also be approached by attempting to predict novel drug-target interactions and then repurposing the drug for the associated indication <span class="citation">[<a href="#ref-tOpadZQw">286</a>,<a href="#ref-1SIuofeg">287</a>]</span>. Wang et al. <span class="citation">[<a href="#ref-TeIxEjqm">288</a>]</span> devised a pairwise input neural network with two hidden layers that takes two inputs, a drug and a target binding site, and predicts whether they interact. Wang et al <span class="citation">[<a href="#ref-1AU7wzPqa">36</a>]</span> trained individual RBMs for each target in a drug-target interaction network and used these models to predict novel interactions pointing to new indications for existing drugs. Wen et al. <span class="citation">[<a href="#ref-oTF8O79C">37</a>]</span> extended this concept to deep learning by creating a DBN of stacked RBMs called DeepDTIs, which is able to predict interactions on the basis of chemical structure and protein sequence features.</p>
-<p>Drug repositioning appears to be an obvious candidate for deep learning both because of the large amount of high-dimensional data available and the complexity of the question being asked. However, what is perhaps the most promising piece of work in this space <span class="citation">[<a href="#ref-EMDwvRGb">35</a>]</span> is more a proof of concept than a real-world hypothesis-generation tool; notably, deep learning was used to predict drug indications but not for the actual repositioning. At present, some of the most popular state-of-the-art methods for signature-based drug repurposing <span class="citation">[<a href="#ref-cQAldRdg">289</a>]</span> do not use predictive modelling. While this might change in the future, we believe that a mature and production-ready framework where deep learning is directly applied to the problem of drug repositioning is currently missing.</p>
+<p>For example, Menden et al. <span class="citation">[<a href="#ref-QcwZC8wG">284</a>]</span> used a shallow neural network to predict sensitivity of cancer cell lines to drug treatment using both cell line and drug features, opening the door to precision medicine and drug repositioning opportunities in cancer. More recently, Aliper et al. <span class="citation">[<a href="#ref-EMDwvRGb">35</a>]</span> used gene- and pathway-level drug perturbation transcriptional profiles from the Library of Network-Based Cellular Signatures (LINCS) <span class="citation">[<a href="#ref-ppGS5h4v">285</a>]</span> to train a fully connected deep neural network to predict drug therapeutic uses and indications. By using confusion matrices and leveraging misclassification, the authors formulated a number of interesting hypotheses, including repurposing cardiovascular drugs such as otenzepad and pinacidil for neurological disorders.</p>
+<p>Drug repositioning can also be approached by attempting to predict novel drug-target interactions and then repurposing the drug for the associated indication <span class="citation">[<a href="#ref-tOpadZQw">286</a>,<a href="#ref-1SIuofeg">287</a>]</span>. Wang et al. <span class="citation">[<a href="#ref-TeIxEjqm">288</a>]</span> devised a pairwise input neural network with two hidden layers that takes two inputs, a drug and a target binding site, and predicts whether they interact. Wang et al. <span class="citation">[<a href="#ref-1AU7wzPqa">36</a>]</span> trained individual RBMs for each target in a drug-target interaction network and used these models to predict novel interactions pointing to new indications for existing drugs. Wen et al. <span class="citation">[<a href="#ref-oTF8O79C">37</a>]</span> extended this concept to deep learning by creating a DBN of stacked RBMs called DeepDTIs, which is able to predict interactions on the basis of chemical structure and protein sequence features.</p>
+<p>Drug repositioning appears to be an obvious candidate for deep learning both because of the large amount of high-dimensional data available and the complexity of the question being asked. However, what is perhaps the most promising piece of work in this space <span class="citation">[<a href="#ref-EMDwvRGb">35</a>]</span> is more of a proof of concept than a real-world hypothesis-generation tool; notably, deep learning was used to predict drug indications but not for the actual repositioning. At present, some of the most popular state-of-the-art methods for signature-based drug repurposing <span class="citation">[<a href="#ref-cQAldRdg">289</a>]</span> do not use predictive modeling. A mature and production-ready framework for drug repositioning via deep learning is currently missing.</p>
 <h3 id="drug-development">Drug development</h3>
 <h4 id="ligand-based-prediction-of-bioactivity">Ligand-based prediction of bioactivity</h4>
-<p>In the biomedical domain, high-throughput chemical screening aims to improve therapeutic options over a long term horizon <span class="citation">[<a href="#ref-1DTUK3YyI">20</a>]</span>. The objective is to discover which small molecules (also referred to as chemical compounds or ligands) effectively and specifically affect the activity of a target, such as a kinase, protein-protein interaction, or broader cellular phenotype. This screening process can serve as one of the first steps in the long drug discovery pipeline, where novel chemicals are pursued for their ability to inhibit or enhance disease-relevant biological mechanisms <span class="citation">[<a href="#ref-RAadmvJN">290</a>]</span>. Initial hits are confirmed to eliminate false positives and proceed to the lead generation stage <span class="citation">[<a href="#ref-1D6emOV6q">291</a>]</span>, where they are evaluated for absorption, distribution, metabolism, excretion, and toxicity (ADMET) and other properties. It is desirable to advance multiple lead series, clusters of structurally-similar active chemicals, for further optimization by medicinal chemists to protect against unexpected failures in the later stages of drug discovery <span class="citation">[<a href="#ref-RAadmvJN">290</a>]</span>.</p>
-<p>The appeal of machine learning in this domain is the need to improve the efficiency of the initial high-throughput screens such that sufficient candidate active compounds can be identified without exhaustively screening libraries of hundreds of thousands or millions of chemicals. Predicting chemical activity computationally is known as virtual screening. This task has been treated variously as a classification, regression, or ranking problem. In reality, it does not fit neatly into any of those categories. An ideal algorithm will rank a sufficient number of active compounds before the inactives, but the rankings of actives relative to other actives and inactives are less important <span class="citation">[<a href="#ref-cjj5vT3H">292</a>]</span>. Computational modeling also has the potential to predict ADMET traits for lead generation <span class="citation">[<a href="#ref-uP7SgBVd">293</a>]</span> and how drugs are metabolized <span class="citation">[<a href="#ref-7QsMcDYy">294</a>]</span>.</p>
-<p>Ligand-based approaches train on chemicals' features without modeling target features (e.g. protein structure). Chemical features may be represented as a list of molecular descriptors such as molecular weight, atom counts, functional groups, charge representations, summaries of atom-atom relationships in the molecular graph, and more sophisticated derived properties <span class="citation">[<a href="#ref-17eGl2pn9">295</a>]</span>. Alternatively, chemicals can be characterized with the fingerprint bit vectors, textual strings, or novel learned representations described below. Neural networks have a long history in this domain <span class="citation">[<a href="#ref-gJE0ExFr">18</a>,<a href="#ref-xPkT1z7D">21</a>]</span>, and the 2012 Merck Molecular Activity Challenge on Kaggle generated substantial excitement about the potential for high-parameter deep learning approaches. The winning submission was an ensemble that included a multi-task multi-layer perceptron network <span class="citation">[<a href="#ref-1Dzz0P0qr">296</a>]</span>. The sponsors noted drastic improvements over a random forest (RF) baseline, remarking &quot;we have seldom seen any method in the past 10 years that could consistently outperform RF by such a margin&quot; <span class="citation">[<a href="#ref-xOaTIeBY">297</a>]</span>. Subsequent work (reviewed in more detail by Goh et al. <span class="citation">[<a href="#ref-zCt6PUXj">19</a>]</span>) explored the effects of jointly modeling far more targets than the Merck challenge <span class="citation">[<a href="#ref-F8fP2vAg">298</a>,<a href="#ref-yAoN5gTU">299</a>]</span>, with Ramsundar et al. <span class="citation">[<a href="#ref-yAoN5gTU">299</a>]</span> showing that the benefits of multi-task networks had not yet saturated even with 259 targets. Although a deep learning approach, DeepTox <span class="citation">[<a href="#ref-Y1D0SZrO">300</a>]</span>, was also the overall winner of another competition, the Toxicology in the 21st Century (Tox21) Data Challenge, it did not dominate alternative methods as thoroughly as in other domains. DeepTox was the top performer on 9 of 15 targets and highly competitive with the top performer on the others. However, for many targets there was little separation between the top two or three methods.</p>
-<p>The nuanced Tox21 performance may be more reflective of the practical challenges encountered in ligand-based chemical screening than the extreme enthusiasm generated by the Merck competition. A study of 22 ADMET tasks demonstrated that there are limitations to multi-task transfer learning that are in part a consequence of the degree to which tasks are related <span class="citation">[<a href="#ref-uP7SgBVd">293</a>]</span>. Some of the ADMET datasets showed superior performance in multi-task models with only 22 ADMET tasks compared to multi-task models with over 500 less-similar tasks. In addition, the training datasets encountered in practical applications may be tiny relative to what is available in public datasets and organized competitions. A study of BACE-1 inhibitors included only 1547 compounds <span class="citation">[<a href="#ref-B4cL1o2P">301</a>]</span>. Machine learning models were able to train on this limited dataset, but overfitting was a challenge and the differences between random forests and a deep neural were negligible, especially in the classification setting. Overfitting is still a problem in larger chemical screening datasets with tens or hundreds of thousands of compounds because the number of active compounds can be very small, on the order of 0.1% of all tested chemicals for a typical target <span class="citation">[<a href="#ref-WeiyYhfy">302</a>]</span>. This is consistent with the strong performance of low-parameter neural networks that emphasize compound-compound similarity, such as influence-relevance voter <span class="citation">[<a href="#ref-cjj5vT3H">292</a>,<a href="#ref-1E0x7QgLP">303</a>]</span>, instead of predicting compound activity directly from chemical features.</p>
-<p>Much of the recent excitement in this domain has come from what could be considered a creative experimentation phase, in which deep learning has offered novel possibilities for feature representation and modeling of chemical compounds. A molecular graph, where atoms are labeled nodes and bonds are labeled edges, is a natural way to represent a chemical structure. Traditional machine learning approaches relied on preprocessing the graph into a feature vector, such as a fixed-width bit vector fingerprint <span class="citation">[<a href="#ref-QnZ7V9Rd">304</a>]</span>. The same fingerprints have been used by some drug-target interaction methods discussed above <span class="citation">[<a href="#ref-oTF8O79C">37</a>]</span>. An overly simplistic but approximately correct view of chemical fingerprints is that each bit represents the presence or absence of a particular chemical substructure in the molecular graph. Modern neural networks can operate directly on the molecular graph as input. Duvenaud et al. <span class="citation">[<a href="#ref-Oe573FaL">305</a>]</span> generalized standard circular fingerprints by substituting discrete operations in the fingerprinting algorithm with operations in a neural network, producing a real-valued feature vector instead of a bit vector. Other approaches offer trainable networks that can in theory learn chemical feature representations that are optimized for a particular prediction task. Lusci et al. <span class="citation">[<a href="#ref-17Wih4Hd5">306</a>]</span> applied recursive neural networks for directed acyclic graphs to undirected molecular graphs by creating an ensemble of directed graphs in which one atom is selected as the root node. A single feature vector is obtained by summing over all feature vectors for all directed graphs in the ensemble. Graph convolutions on undirected molecular graphs have eliminated the need to enumerate artificially directed graphs, learning feature vectors for atoms that are a function of the properties of neighboring atoms and local regions on the molecular graph <span class="citation">[<a href="#ref-145os4Y6t">307</a>,<a href="#ref-P4ixsM8i">308</a>]</span>.</p>
+<p>In the biomedical domain, high-throughput chemical screening aims to improve therapeutic options over a long term horizon <span class="citation">[<a href="#ref-1DTUK3YyI">20</a>]</span>. The objective is to discover which small molecules (also referred to as chemical compounds or ligands) that specifically affect the activity of a target, such as a kinase, protein-protein interaction, or broader cellular phenotype. This screening is often one of the first steps in a long drug discovery pipeline, where novel molecules are pursued for their ability to inhibit or enhance disease-relevant biological mechanisms <span class="citation">[<a href="#ref-RAadmvJN">290</a>]</span>. Initial hits are confirmed to eliminate false positives and proceed to the lead generation stage <span class="citation">[<a href="#ref-1D6emOV6q">291</a>]</span>, where they are evaluated for absorption, distribution, metabolism, excretion, and toxicity (ADMET) and other properties. It is desirable to advance multiple lead series, clusters of structurally-similar active chemicals, for further optimization by medicinal chemists to protect against unexpected failures in the later stages of drug discovery <span class="citation">[<a href="#ref-RAadmvJN">290</a>]</span>.</p>
+<p>Computational work in this domain aims to identify sufficient candidate active compounds without exhaustively screening libraries of hundreds of thousands or millions of chemicals. Predicting chemical activity computationally is known as virtual screening. This task has been treated variously as a classification, regression, or ranking problem. In reality, it does not fit neatly into any of those categories. An ideal algorithm will rank a sufficient number of active compounds before the inactives, but the rankings of actives relative to other actives and inactives are less important <span class="citation">[<a href="#ref-cjj5vT3H">292</a>]</span>. Computational modeling also has the potential to predict ADMET traits for lead generation <span class="citation">[<a href="#ref-uP7SgBVd">293</a>]</span> and how drugs are metabolized <span class="citation">[<a href="#ref-7QsMcDYy">294</a>]</span>.</p>
+<p>Ligand-based approaches train on chemicals' features without modeling target features (e.g. protein structure). Chemical features may be represented as a list of molecular descriptors such as molecular weight, atom counts, functional groups, charge representations, summaries of atom-atom relationships in the molecular graph, and more sophisticated derived properties <span class="citation">[<a href="#ref-17eGl2pn9">295</a>]</span>. Alternatively, chemicals can be characterized with the fingerprint bit vectors, textual strings, or novel learned representations described below. Neural networks have a long history in this domain <span class="citation">[<a href="#ref-gJE0ExFr">18</a>,<a href="#ref-xPkT1z7D">21</a>]</span>, and the 2012 Merck Molecular Activity Challenge on Kaggle generated substantial excitement about the potential for high-parameter deep learning approaches. The winning submission was an ensemble that included a multi-task multi-layer perceptron network <span class="citation">[<a href="#ref-1Dzz0P0qr">296</a>]</span>. The sponsors noted drastic improvements over a random forest (RF) baseline, remarking &quot;we have seldom seen any method in the past 10 years that could consistently outperform RF by such a margin&quot; <span class="citation">[<a href="#ref-xOaTIeBY">297</a>]</span>. Subsequent work (reviewed in more detail by Goh et al. <span class="citation">[<a href="#ref-zCt6PUXj">19</a>]</span>) explored the effects of jointly modeling far more targets than the Merck challenge <span class="citation">[<a href="#ref-F8fP2vAg">298</a>,<a href="#ref-yAoN5gTU">299</a>]</span>, with Ramsundar et al. <span class="citation">[<a href="#ref-yAoN5gTU">299</a>]</span> showing that the benefits of multi-task networks had not yet saturated even with 259 targets. Although DeepTox <span class="citation">[<a href="#ref-Y1D0SZrO">300</a>]</span>, a deep learning approach, won another competition, the Toxicology in the 21st Century (Tox21) Data Challenge, it did not dominate alternative methods as thoroughly as in other domains. DeepTox was the top performer on 9 of 15 targets and highly competitive with the top performer on the others. However, for many targets there was little separation between the top two or three methods.</p>
+<p>The nuanced Tox21 performance may be more reflective of the practical challenges encountered in ligand-based chemical screening than the extreme enthusiasm generated by the Merck competition. A study of 22 ADMET tasks demonstrated that there are limitations to multi-task transfer learning that are in part a consequence of the degree to which tasks are related <span class="citation">[<a href="#ref-uP7SgBVd">293</a>]</span>. Some of the ADMET datasets showed superior performance in multi-task models with only 22 ADMET tasks compared to multi-task models with over 500 less-similar tasks. In addition, the training datasets encountered in practical applications may be tiny relative to what is available in public datasets and organized competitions. A study of BACE-1 inhibitors included only 1547 compounds <span class="citation">[<a href="#ref-B4cL1o2P">301</a>]</span>. Machine learning models were able to train on this limited dataset, but overfitting was a challenge and the differences between random forests and a deep neural network were negligible, especially in the classification setting. Overfitting is still a problem in larger chemical screening datasets with tens or hundreds of thousands of compounds because the number of active compounds can be very small, on the order of 0.1% of all tested chemicals for a typical target <span class="citation">[<a href="#ref-WeiyYhfy">302</a>]</span>. This is consistent with the strong performance of low-parameter neural networks that emphasize compound-compound similarity, such as influence-relevance voter <span class="citation">[<a href="#ref-cjj5vT3H">292</a>,<a href="#ref-1E0x7QgLP">303</a>]</span>, instead of predicting compound activity directly from chemical features.</p>
+<p>Much of the recent excitement in this domain has come from what could be considered a creative experimentation phase, in which deep learning has offered novel possibilities for feature representation and modeling of chemical compounds. A molecular graph, where atoms are labeled nodes and bonds are labeled edges, is a natural way to represent a chemical structure. Traditional machine learning approaches relied on preprocessing the graph into a feature vector, such as a fixed-width bit vector fingerprint <span class="citation">[<a href="#ref-QnZ7V9Rd">304</a>]</span>. The same fingerprints have been used by some drug-target interaction methods discussed above <span class="citation">[<a href="#ref-oTF8O79C">37</a>]</span>. An overly simplistic but approximately correct view of chemical fingerprints is that each bit represents the presence or absence of a particular chemical substructure in the molecular graph. Modern neural networks can operate directly on the molecular graph as input. Duvenaud et al. <span class="citation">[<a href="#ref-Oe573FaL">305</a>]</span> generalized standard circular fingerprints by substituting discrete operations in the fingerprinting algorithm with operations in a neural network, producing a real-valued feature vector instead of a bit vector. Other approaches offer trainable networks that can learn chemical feature representations that are optimized for a particular prediction task. Lusci et al. <span class="citation">[<a href="#ref-17Wih4Hd5">306</a>]</span> applied recursive neural networks for directed acyclic graphs to undirected molecular graphs by creating an ensemble of directed graphs in which one atom is selected as the root node. Graph convolutions on undirected molecular graphs have eliminated the need to enumerate artificially directed graphs, learning feature vectors for atoms that are a function of the properties of neighboring atoms and local regions on the molecular graph <span class="citation">[<a href="#ref-145os4Y6t">307</a>,<a href="#ref-P4ixsM8i">308</a>]</span>.</p>
 <p>Advances in chemical representation learning have also enabled new strategies for learning chemical-chemical similarity functions. Altae-Tran et al. developed a one-shot learning network <span class="citation">[<a href="#ref-P4ixsM8i">308</a>]</span> to address the reality that most practical chemical screening studies are unable to provide the thousands or millions of training compounds that are needed to train larger multi-task networks. Using graph convolutions to featurize chemicals, the network learns an embedding from compounds into a continuous feature space such that compounds with similar activities in a set of training tasks have similar embeddings. The approach is evaluated in an extremely challenging setting. The embedding is learned from a subset of prediction tasks (e.g. activity assays for individual proteins), and only one to ten labeled examples are provided as training data on a new task. On Tox21 targets, even when trained with <em>one</em> task-specific active compound and <em>one</em> inactive compound, the model is able to generalize reasonably well because it has learned an informative embedding function from the related tasks. Random forests, which cannot take advantage of the related training tasks, trained in the same setting are only slightly better than a random classifier. Despite the success on Tox21, performance on MUV datasets, which contains assays designed to be challenging for chemical informatics algorithms, is considerably worse. The authors also demonstrate the limitations of transfer learning as embeddings learned from the Tox21 assays have little utility for a drug adverse reaction dataset.</p>
-<p>These novel, learned chemical feature representations may prove to be essential for accurately predicting why some compounds with similar structures yield similar target effects and others produce drastically different results. Currently, these methods are enticing but do not necessarily outperform classic approaches by a large margin. The neural fingerprints <span class="citation">[<a href="#ref-Oe573FaL">305</a>]</span> were narrowly beaten by regression using traditional circular fingerprints on a drug efficacy prediction task (but were superior for predicting solubility or photovoltaic efficiency). In the original study, graph convolutions <span class="citation">[<a href="#ref-145os4Y6t">307</a>]</span> performed comparably to a multitask network using standard fingerprints and slightly better than the neural fingerprints <span class="citation">[<a href="#ref-Oe573FaL">305</a>]</span> on the drug efficacy task but were slightly worse than the influence-relevance voter method on an HIV dataset. <span class="citation">[<a href="#ref-cjj5vT3H">292</a>]</span>. Broader recent benchmarking has shown that relative merits of these methods depends on the dataset and cross validation strategy <span class="citation">[<a href="#ref-16OPHvAij">309</a>]</span>, though we caution against over-interpreting AUC ROC-based results, a popular metric in this domain despite the active/inactive class imbalance (see Discussion).</p>
-<p>We remain optimistic for the potential of deep learning and specifically representation learning in drug discovery and propose that rigorous benchmarking on broad and diverse prediction tasks will be as important as novel neural network architectures to advance the state of the art and convincingly demonstrate superiority over traditional cheminformatics techniques. Fortunately, there has recently been much progress in this direction. The DeepChem software <span class="citation">[<a href="#ref-P4ixsM8i">308</a>,<a href="#ref-Ytvk62dX">310</a>]</span> and MoleculeNet benchmarking suite <span class="citation">[<a href="#ref-16OPHvAij">309</a>]</span> built upon it contain chemical bioactivity and toxicity prediction datasets, multiple compound featurization approaches including graph convolutions, and various machine learning algorithms ranging from standard baselines like logistic regression and random forests to recent neural network architectures. Independent research groups have already contributed additional datasets and prediction algorithms to DeepChem, and adoption of common benchmarking evaluation metrics, datasets, and baseline algorithms has the potential to establish the practical utility of deep learning in chemical bioactivity prediction and lower the barrier to entry for machine learning researchers without biochemistry expertise.</p>
+<p>These novel, learned chemical feature representations may prove to be essential for accurately predicting why some compounds with similar structures yield similar target effects and others produce drastically different results. Currently, these methods are enticing but do not necessarily outperform classic approaches by a large margin. The neural fingerprints <span class="citation">[<a href="#ref-Oe573FaL">305</a>]</span> were narrowly beaten by regression using traditional circular fingerprints on a drug efficacy prediction task but were superior for predicting solubility or photovoltaic efficiency. In the original study, graph convolutions <span class="citation">[<a href="#ref-145os4Y6t">307</a>]</span> performed comparably to a multitask network using standard fingerprints and slightly better than the neural fingerprints <span class="citation">[<a href="#ref-Oe573FaL">305</a>]</span> on the drug efficacy task but were slightly worse than the influence-relevance voter method on an HIV dataset. <span class="citation">[<a href="#ref-cjj5vT3H">292</a>]</span>. Broader recent benchmarking has shown that relative merits of these methods depends on the dataset and cross validation strategy <span class="citation">[<a href="#ref-16OPHvAij">309</a>]</span>, though evaluation often uses AUC ROC, which has limited utility due to the large active/inactive class imbalance (see Discussion).</p>
+<p>We remain optimistic for the potential of deep learning and specifically representation learning in drug discovery and propose that rigorous benchmarking on broad and diverse prediction tasks will be as important as novel neural network architectures to advance the state of the art and convincingly demonstrate superiority over traditional cheminformatics techniques. Fortunately, there has recently been much progress in this direction. The DeepChem software <span class="citation">[<a href="#ref-P4ixsM8i">308</a>,<a href="#ref-Ytvk62dX">310</a>]</span> and MoleculeNet benchmarking suite <span class="citation">[<a href="#ref-16OPHvAij">309</a>]</span> built upon it contain chemical bioactivity and toxicity prediction datasets, multiple compound featurization approaches including graph convolutions, and various machine learning algorithms ranging from standard baselines like logistic regression and random forests to recent neural network architectures. Independent research groups have already contributed additional datasets and prediction algorithms to DeepChem. Adoption of common benchmarking evaluation metrics, datasets, and baseline algorithms has the potential to establish the practical utility of deep learning in chemical bioactivity prediction and lower the barrier to entry for machine learning researchers without biochemistry expertise.</p>
 <p>One open question in ligand-based screening pertains to the benefits and limitations of transfer learning. Multi-task neural networks have shown the advantages of jointly modeling many targets <span class="citation">[<a href="#ref-F8fP2vAg">298</a>,<a href="#ref-yAoN5gTU">299</a>]</span>. Other studies have shown the limitations of transfer learning when the prediction tasks are insufficiently related <span class="citation">[<a href="#ref-uP7SgBVd">293</a>,<a href="#ref-P4ixsM8i">308</a>]</span>. This has important implications for representation learning. The typical approach to improve deep learning models by expanding the dataset size may not be applicable if only &quot;related&quot; tasks are beneficial, especially because task-task relatedness is ill-defined. The massive chemical state space will also influence the development of unsupervised representation learning methods <span class="citation">[<a href="#ref-2dU8f4XJ">311</a>]</span>. Future work will establish whether it is better to train on massive collections of diverse compounds, drug-like small molecules, or specialized subsets.</p>
 <h4 id="structure-based-prediction-of-bioactivity">Structure-based prediction of bioactivity</h4>
 <p>When protein structure is available, virtual screening has traditionally relied on docking programs to predict how a compound best fits in the target's binding site and score the predicted ligand-target complex <span class="citation">[<a href="#ref-13iyYvEcB">312</a>]</span>. Recently, deep learning approaches have been developed to model protein structure, which is expected to improve upon the simpler drug-target interaction algorithms described above that represent proteins with feature vectors derived from amino acid sequences <span class="citation">[<a href="#ref-oTF8O79C">37</a>,<a href="#ref-TeIxEjqm">288</a>]</span>.</p>
-<p>Structure-based deep learning methods differ in whether they use experimentally-derived or predicted ligand-target complexes and how they represent the 3D structure. The Atomic Convolutional Neural Network <span class="citation">[<a href="#ref-17YaKNLKk">313</a>]</span> takes 3D crystal structures from PDBBind <span class="citation">[<a href="#ref-YO41GAOP">314</a>]</span> as input, ensuring it uses a reliable ligand-target complex. Alternatively, AtomNet <span class="citation">[<a href="#ref-Z7fd0BYf">34</a>]</span> samples multiple ligand poses within the target binding site, and DeepVS <span class="citation">[<a href="#ref-Gue0c5Gb">315</a>]</span> and Ragoza et al. <span class="citation">[<a href="#ref-bNBiIiTt">316</a>]</span> use a docking program to generate protein-compound complexes. If they are sufficiently accurate, these latter approaches would have wider applicability to a much larger set of compounds and proteins. However, incorrect ligand poses will be misleading during training, and the predictive performance is sensitive to the docking quality <span class="citation">[<a href="#ref-Gue0c5Gb">315</a>]</span>.</p>
-<p>There are two major options for representing a protein-compound complex. A 3D grid can be used to featurize the input complex <span class="citation">[<a href="#ref-Z7fd0BYf">34</a>,<a href="#ref-bNBiIiTt">316</a>]</span>. Each entry in the grid tracks the types of protein and ligand atoms in that region of the 3D space or descriptors derived from those atoms. Both DeepVS <span class="citation">[<a href="#ref-Gue0c5Gb">315</a>]</span> and atomic convolutions <span class="citation">[<a href="#ref-17YaKNLKk">313</a>]</span> offer greater flexibility in their convolutions by eschewing the 3D grid. Instead, they each implement techniques for executing convolutions over atoms' neighboring atoms in the 3D space. Gomes et al. demonstrate that currently random forest on a 1D feature vector that describes the 3D ligand-target structure generally outperforms neural networks on the same feature vector as well as atomic convolutions and ligand-based neural networks when predicting the continuous-valued inhibition constant on the PDBBind refined dataset <span class="citation">[<a href="#ref-17YaKNLKk">313</a>]</span>. However, in the long term, atomic convolutions may ultimately overtake grid-based methods, as they provide greater freedom to model atom-atom interactions and the forces that govern binding affinity.</p>
+<p>Structure-based deep learning methods differ in whether they use experimentally-derived or predicted ligand-target complexes and how they represent the 3D structure. The Atomic CNN <span class="citation">[<a href="#ref-17YaKNLKk">313</a>]</span> takes 3D crystal structures from PDBBind <span class="citation">[<a href="#ref-YO41GAOP">314</a>]</span> as input, ensuring it uses a reliable ligand-target complex. AtomNet <span class="citation">[<a href="#ref-Z7fd0BYf">34</a>]</span> samples multiple ligand poses within the target binding site, and DeepVS <span class="citation">[<a href="#ref-Gue0c5Gb">315</a>]</span> and Ragoza et al. <span class="citation">[<a href="#ref-bNBiIiTt">316</a>]</span> use a docking program to generate protein-compound complexes. If they are sufficiently accurate, these latter approaches would have wider applicability to a much larger set of compounds and proteins. However, incorrect ligand poses will be misleading during training, and the predictive performance is sensitive to the docking quality <span class="citation">[<a href="#ref-Gue0c5Gb">315</a>]</span>.</p>
+<p>There are two major options for representing a protein-compound complex. A 3D grid can featurize the input complex <span class="citation">[<a href="#ref-Z7fd0BYf">34</a>,<a href="#ref-bNBiIiTt">316</a>]</span>. Each entry in the grid tracks the types of protein and ligand atoms in that region of the 3D space or descriptors derived from those atoms. Both DeepVS <span class="citation">[<a href="#ref-Gue0c5Gb">315</a>]</span> and atomic convolutions <span class="citation">[<a href="#ref-17YaKNLKk">313</a>]</span> offer greater flexibility in their convolutions by eschewing the 3D grid. Instead, they each implement techniques for executing convolutions over atoms' neighboring atoms in the 3D space. Gomes et al. demonstrate that currently random forest on a 1D feature vector that describes the 3D ligand-target structure generally outperforms neural networks on the same feature vector as well as atomic convolutions and ligand-based neural networks when predicting the continuous-valued inhibition constant on the PDBBind refined dataset <span class="citation">[<a href="#ref-17YaKNLKk">313</a>]</span>. However, in the long term, atomic convolutions may ultimately overtake grid-based methods, as they provide greater freedom to model atom-atom interactions and the forces that govern binding affinity.</p>
 <h4 id="de-novo-drug-design"><em>De novo</em> drug design</h4>
 <p>Whereas the goal of virtual screening is to find active molecules by predicting the biochemical activity of hundreds of thousands to millions of chemicals using existing (virtual) chemical libraries, analogous to robotic high-throughput &quot;wet lab&quot; screening, <em>de novo</em> drug design aims to directly <em>generate</em> active compounds <span class="citation">[<a href="#ref-kJ4hy7E">317</a>,<a href="#ref-omzv9ryI">318</a>]</span>.</p>
-<p>Drug design attempts to model the typical design-synthesize-test cycle of drug discovery <span class="citation">[<a href="#ref-kJ4hy7E">317</a>]</span>. Thus <em>de novo</em> design explores in principle without explicit enumeration the much larger space of an estimated 10<sup>60</sup> synthesizable organic molecules with drug-like properties <span class="citation">[<a href="#ref-WeiyYhfy">302</a>]</span>. To test or score structures, machine learning algorithms like those discussed earlier are used. To &quot;design&quot; and &quot;synthesize&quot;, traditional <em>de novo</em> design software relied on classical optimizers such as genetic algorithms. Unfortunately, this often leads to overfit, &quot;weird&quot; molecules, which are difficult to synthesize in the lab. To generate molecules, current programs have therefore settled on rule-based virtual chemical reactions to generate molecular structures <span class="citation">[<a href="#ref-omzv9ryI">318</a>]</span>.</p>
-<p>Deep learning models that learn to generate realistic, synthesizable molecules have been proposed as an alternative to provide the large molecule sets needed for virtual screening or even to create and refine focused molecules for <em>de novo</em> design. In contrast to the classical, symbolic approaches, generative models learned from data do not depend on laboriously encoded expert knowledge. The problem is related to the generation of syntactically and semantically correct text <span class="citation">[<a href="#ref-15y7iq6HF">319</a>]</span>.</p>
+<p>Drug design attempts to model the typical design-synthesize-test cycle of drug discovery <span class="citation">[<a href="#ref-kJ4hy7E">317</a>]</span>. <em>De novo</em> design explores the much larger space of an estimated 10<sup>60</sup> synthesizable organic molecules with drug-like properties without explicit enumeration <span class="citation">[<a href="#ref-WeiyYhfy">302</a>]</span>. To test or score structures, algorithms like those discussed earlier are used. To &quot;design&quot; and &quot;synthesize&quot;, traditional <em>de novo</em> design software relied on classical optimizers such as genetic algorithms. Unfortunately, this often leads to overfit, &quot;weird&quot; molecules, which are difficult to synthesize in the lab. Current programs have settled on rule-based virtual chemical reactions to generate molecular structures <span class="citation">[<a href="#ref-omzv9ryI">318</a>]</span>. Deep learning models that generate realistic, synthesizable molecules have been proposed as an alternative. In contrast to the classical, symbolic approaches, generative models learned from data would not depend on laboriously encoded expert knowledge. The challenge of generating molecules has parallels to the generation of syntactically and semantically correct text <span class="citation">[<a href="#ref-15y7iq6HF">319</a>]</span>.</p>
 <p>As deep learning models that directly output (molecular) graphs remain under-explored, generative neural networks for drug design typically represent chemicals with the simplified molecular-input line-entry system (SMILES), a standard string-based representation with characters that represent atoms, bonds, and rings <span class="citation">[<a href="#ref-8LWFFeYg">320</a>]</span>. This allows treating molecules as sequences and leveraging recent progress in recurrent neural networks. Gómez-Bombarelli et al. designed a SMILES-to-SMILES autoencoder to learn a continuous latent feature space for chemicals <span class="citation">[<a href="#ref-2dU8f4XJ">311</a>]</span>. In this learned continuous space it was possible to interpolate between continuous representations of chemicals in a manner that is not possible with discrete (e.g. bit vector or string) features or in symbolic, molecular graph space. Even more interesting is the prospect of performing gradient-based or Bayesian optimization of molecules within this latent space. The strategy of constructing simple, continuous features before applying supervised learning techniques is reminiscent of autoencoders trained on high-dimensional EHR data <span class="citation">[<a href="#ref-5x3uMSKi">81</a>]</span>. A drawback of the SMILES-to-SMILES autoencoder is that not all SMILES strings produced by the autoencoder's decoder correspond to valid chemical structures. Recently, the Grammar Variational Autoencoder, which takes the SMILES grammar into account and is guaranteed to produce syntactically valid SMILES, has been proposed to alleviate this issue <span class="citation">[<a href="#ref-AQ3N6Ayw">321</a>]</span>.</p>
-<p>Another approach to drug design is to train character-based RNNs on large collections of molecules, for example, ChEMBL <span class="citation">[<a href="#ref-x1nE5icc">322</a>]</span>, to first obtain a generic generative model for drug-like compounds <span class="citation">[<a href="#ref-8LWFFeYg">320</a>]</span>. These generative models successfully learn the grammar of compound representations, with 94% <span class="citation">[<a href="#ref-1EayJRsI">323</a>]</span> or nearly 98% <span class="citation">[<a href="#ref-8LWFFeYg">320</a>]</span> of generated SMILES corresponding to valid molecular structures. The initial RNN is then fine-tuned to generate molecules that are likely to be active against a specific target by either continuing training on a small set of positive examples <span class="citation">[<a href="#ref-8LWFFeYg">320</a>]</span> or adopting reinforcement learning strategies <span class="citation">[<a href="#ref-1EayJRsI">323</a>,<a href="#ref-lERqKdZJ">324</a>]</span>. Both the fine-tuning and reinforcement learning approaches can rediscover known, held-out active molecules. The great flexibility of neural networks, and progress in generative models offers many opportunities for deep architectures in <em>de novo</em> design, for example, the adaptation of Generative Adversarial Networks (GANs) for molecules.</p>
+<p>Another approach to drug design is to train character-based RNNs on large collections of molecules, for example, ChEMBL <span class="citation">[<a href="#ref-x1nE5icc">322</a>]</span>, to first obtain a generic generative model for drug-like compounds <span class="citation">[<a href="#ref-8LWFFeYg">320</a>]</span>. These generative models successfully learn the grammar of compound representations, with 94% <span class="citation">[<a href="#ref-1EayJRsI">323</a>]</span> or nearly 98% <span class="citation">[<a href="#ref-8LWFFeYg">320</a>]</span> of generated SMILES corresponding to valid molecular structures. The initial RNN is then fine-tuned to generate molecules that are likely to be active against a specific target by either continuing training on a small set of positive examples <span class="citation">[<a href="#ref-8LWFFeYg">320</a>]</span> or adopting reinforcement learning strategies <span class="citation">[<a href="#ref-1EayJRsI">323</a>,<a href="#ref-lERqKdZJ">324</a>]</span>. Both the fine-tuning and reinforcement learning approaches can rediscover known, held-out active molecules. The great flexibility of neural networks, and progress in generative models offers many opportunities for deep architectures in <em>de novo</em> design (e.g. the adaptation of Generative Adversarial Networks (GANs) for molecules).</p>
 <h2 id="discussion">Discussion</h2>
 <p>Despite the disparate types of data and scientific goals in the learning tasks covered above, several challenges are broadly important for deep learning in the biomedical domain. Here we examine these factors that may impede further progress, ask what steps have already been taken to overcome them, and suggest future research directions.</p>
 <h3 id="evaluation">Evaluation</h3>
-<p>There are unique challenges to evaluating deep learning predictions in the biomedical domain. We focus on TF binding prediction as a representative task to illustrate some of these issues. The human genome has 3E9 base pairs, and only a small fraction of them are implicated in specific biochemical activities. As a result, classification of genomic regions based on their biochemical activity results in highly imbalanced classification. Class imbalance also arises in other problems we review, such as virtual screening for drug discovery. What are appropriate evaluation metrics that account for the label imbalance? The classification labels are formulated based on continuous value experimental signals. Practitioners must determine an appropriate procedure for formulating binary classification labels based on these signals. In addition, the experimental signals are only partially reproducible across experimental replicates. An appropriate upper bound for classification performance must account for the experimental reproducibility.</p>
+<p>There are unique challenges to evaluating deep learning predictions in the biomedical domain. We focus on TF binding prediction as a representative task to illustrate some of these issues. The human genome has 3 billion base pairs, and only a small fraction of them are implicated in specific biochemical activities. As a result, classification of genomic regions based on their biochemical activity results in highly imbalanced classification. Class imbalance also arises in other problems we review, such as virtual screening for drug discovery. What are appropriate evaluation metrics that account for the label imbalance? The classification labels are formulated based on continuous value experimental signals. Practitioners must determine an appropriate procedure for formulating binary classification labels based on these signals. In addition, the experimental signals are only partially reproducible across experimental replicates. An appropriate upper bound for classification performance must account for the experimental reproducibility.</p>
 <h4 id="evaluation-metrics-for-imbalanced-classification">Evaluation metrics for imbalanced classification</h4>
 <p>Less than 1% of the genome can be confidently labeled as bound for most transcription factors. Therefore, it is important to evaluate the genome-wide recall and false discovery rate (FDR) of classification models of biochemical activities. Targeted validation experiments of specific biochemical activities usually necessitate an FDR of 5-25%. When predicted biochemical activities are used as features in other models, such as gene expression models, a low FDR may not be as critical if the downstream models can satisfy their evaluation criteria. An FDR of 50% in this context may suffice.</p>
-<p>What is the correspondence between these metrics and commonly used classification metrics such as auPRC and auROC? auPRC evaluates the average precision, or equivalently, the average FDR across all recall thresholds. This metric provides an overall estimate of performance across all possible use cases, which can be misleading for targeted validation experiments. For example, classification of TF binding sites can exhibit a recall of 0% at 10% FDR and auPRC greater than 0.6. In this case, the auPRC may be competitive, but the predictions are ill-suited for targeted validation that can only examine a few of the highest-confidence predictions. Likewise, auROC evaluates the average recall across all false positive rate (FPR) thresholds, which is often a highly misleading metric in class-imbalanced domains <span class="citation">[<a href="#ref-JmHFuXEM">71</a>,<a href="#ref-JNnkm5Zt">325</a>]</span>. For example, consider a classification model with recall of 0% at FDR less than 25% and 100% recall at FDR greater than 25%. In the context of TF binding predictions where only 1% of genomic regions are bound by the TF, this is equivalent to a recall of 100% for FPR greater than 0.33%. In other words, the auROC would be 0.9967 and not indicative of the predictive utility for targeted validation. It is not unusual to obtain a chromosome-wide auROC greater than 0.99 for TF binding predictions but a recall of 0% at 10% FDR. The classifier would appear to be nearly perfect based on auROC but has no practical utility to prioritize high-confidence binding sites for experimental validation.</p>
+<p>What is the correspondence between these metrics and commonly used classification metrics such as auPRC and auROC? auPRC evaluates the average precision, or equivalently, the average FDR across all recall thresholds. This metric provides an overall estimate of performance across all possible use cases, which can be misleading for targeted validation experiments. For example, classification of TF binding sites can exhibit a recall of 0% at 10% FDR and auPRC greater than 0.6. In this case, the auPRC may be competitive, but the predictions are ill-suited for targeted validation that can only examine a few of the highest-confidence predictions. Likewise, auROC evaluates the average recall across all false positive rate (FPR) thresholds, which is often a highly misleading metric in class-imbalanced domains <span class="citation">[<a href="#ref-JmHFuXEM">71</a>,<a href="#ref-JNnkm5Zt">325</a>]</span>. For example, consider a classification model with recall of 0% at FDR less than 25% and 100% recall at FDR greater than 25%. In the context of TF binding predictions where only 1% of genomic regions are bound by the TF, this is equivalent to a recall of 100% for FPR greater than 0.33%. In other words, the auROC would be 0.9967, but the classifier would be useless for targeted validation. It is not unusual to obtain a chromosome-wide auROC greater than 0.99 for TF binding predictions but a recall of 0% at 10% FDR.</p>
 <h4 id="formulation-of-classification-labels">Formulation of classification labels</h4>
-<p>Genome-wide continuous signals are commonly formulated into classification labels through signal peak detection. For example, peaks in ChIP-seq signal are used to identify locations of TF binding and histone modifications. Such procedures rely on thresholding criteria to define what constitutes a peak in the signal. This inevitably results in a set of signal peaks that are close to the threshold, not sufficient to constitute a positive label but too similar to positively labeled examples to constitute a negative label. To avoid an arbitrary label for these example they may be labeled as &quot;ambiguous&quot;. Ambiguously labeled examples can then be ignored during model training and evaluation of recall and FDR. The correlation between model predictions on these examples and their signal values can be used to evaluate if the model correctly ranks these examples between positive and negative examples.</p>
+<p>Genome-wide continuous signals are commonly formulated into classification labels through signal peak detection. ChIP-seq peaks are used to identify locations of TF binding and histone modifications. Such procedures rely on thresholding criteria to define what constitutes a peak in the signal. This inevitably results in a set of signal peaks that are close to the threshold, not sufficient to constitute a positive label but too similar to positively labeled examples to constitute a negative label. To avoid an arbitrary label for these example they may be labeled as &quot;ambiguous&quot;. Ambiguously labeled examples can then be ignored during model training and evaluation of recall and FDR. The correlation between model predictions on these examples and their signal values can be used to evaluate if the model correctly ranks these examples between positive and negative examples.</p>
 <h4 id="formulation-of-a-performance-upper-bound">Formulation of a performance upper bound</h4>
-<p>Genome-wide signals across experiments can lead to different sets of positive examples. When experimental replicates do not completely agree, perfect recall at a low FDR is not possible. The upper bound on the recall is the fraction of positive examples that are in agreement across experiments. This fraction will vary depending on the available experimental data. For example, reproducibility for experimental replicates from the same lab is typically higher than experimental replicates across multiple labs. One way to handle the range of reproducibility is the use of multiple reproducibility criteria such as reproducibility across technical replicates, biological replicates from the same lab, and biological replicates from multiple labs.</p>
+<p>Genome-wide signals across experiments can lead to different sets of positive examples. When experimental replicates do not completely agree, perfect recall at a low FDR is not possible. The upper bound on the recall is the fraction of positive examples that are in agreement across experiments. This fraction will vary depending on the available experimental data. Reproducibility for experimental replicates from the same lab is typically higher than experimental replicates across multiple labs. One way to handle the range of reproducibility is the use of multiple reproducibility criteria such as reproducibility across technical replicates, biological replicates from the same lab, and biological replicates from multiple labs.</p>
 <h3 id="interpretation">Interpretation</h3>
 <p>As deep learning models achieve state-of-the-art performance in a variety of domains, there is a growing need to make the models more interpretable. Interpretability matters for two main reasons. First, a model that achieves breakthrough performance may have identified patterns in the data that practitioners in the field would like to understand. However, this would not be possible if the model is a black box. Second, interpretability is important for trust. If a model is making medical diagnoses, it is important to ensure the model is making decisions for reliable reasons and is not focusing on an artifact of the data. A motivating example of this can be found in Ba and Caruana <span class="citation">[<a href="#ref-1AhGoHZP9">326</a>]</span>, where a model trained to predict the likelihood of death from pneumonia assigned lower risk to patients with asthma, but only because such patients were treated as higher priority by the hospital. In the context of deep learning, understanding the basis of a model's output is particularly important as deep learning models are unusually susceptible to adversarial examples <span class="citation">[<a href="#ref-1AkF8Wsv7">327</a>]</span> and can output confidence scores over 99.99% for samples that resemble pure noise.</p>
 <p>As the concept of interpretability is quite broad, many methods described as improving the interpretability of deep learning models take disparate and often complementary approaches. Some key themes are discussed below.</p>
 <h4 id="assigning-example-specific-importance-scores">Assigning example-specific importance scores</h4>
 <p>Several approaches ascribe importance on an example-specific basis to the parts of the input that are responsible for a particular output. These can be broadly divided into perturbation-based approaches and backpropagation-based approaches.</p>
-<h5 id="perturbation-based-approaches">Perturbation-based approaches</h5>
-<p>These approaches make perturbations to parts of the input and observe the impact on the output of the network. Alipanahi et al. <span class="citation">[<a href="#ref-jJHZHWrl">161</a>]</span> and Zhou &amp; Troyanskaya <span class="citation">[<a href="#ref-2UI1BZuD">165</a>]</span> scored genomic sequences by introducing virtual mutations at individual positions in the sequence and quantifying the change in the output. Umarov et al. <span class="citation">[<a href="#ref-as2HfoSh">175</a>]</span> used a similar strategy, but with sliding windows where the sequence within each sliding window was substituted with a random sequence. Kelley et al. <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span> inserted known protein-binding motifs into the centers of sequences and assessed the change in predicted accessibility.</p>
-<p>Ribeiro et al. <span class="citation">[<a href="#ref-QwXSJhr0">328</a>]</span> introduced LIME, which constructs a linear model to locally approximate the output of the network on perturbed versions of the input and assigns importance scores accordingly. For analyzing images, Zeiler and Fergus <span class="citation">[<a href="#ref-voh0OiT2">329</a>]</span> applied constant-value masks to different input patches and studied the changes in the activations of later layers. As an alternative to using masks, which can look artificial compared to typical values for an input patch, Zintgraf et al. <span class="citation">[<a href="#ref-Kk20paR7">330</a>]</span> proposed a novel strategy based on marginalizing over plausible values of an input patch to more accurately estimate its contribution.</p>
-<p>A common drawback to perturbation-based approaches is computational efficiency: each perturbed version of an input requires a separate forward propagation through the network to compute the output. As noted by Shrikumar et al. <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>]</span>, such methods may also underestimate the impact of features that have saturated their contribution to the output, as can happen when multiple redundant features are present.</p>
-<p>To reduce the computational overhead of perturbation-based approaches, Fong and Vedaldi <span class="citation">[<a href="#ref-y4t9EzPn">331</a>]</span> solve an optimization problem using gradient descent to discover a minimal subset of inputs to perturb in order to decrease the predicted probability of a selected class. When tested on image data, their method took about 300 iterations to converge, compared to the ~5000 iterations used by LIME. One drawback of this approach is that the use of gradient descent requires the perturbation to have a differentiable form.</p>
-<h5 id="backpropagation-based-approaches">Backpropagation-based approaches</h5>
-<p>A second strategy for addressing the computational inefficiency of perturbation-based approaches is to propagate an important signal from a target output neuron backwards through the layers to the input layer in a single backpropagation-like pass. A classic example of this is calculating the gradients of the output with respect to the input <span class="citation">[<a href="#ref-1YcKYTvO">332</a>]</span> to compute a &quot;saliency map&quot;. Bach et al. <span class="citation">[<a href="#ref-au5CLIOH">333</a>]</span> proposed a strategy called Layerwise Relevance Propagation, which was shown to be equivalent to the elementwise product of the gradient and input <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>,<a href="#ref-b1sc0cgP">334</a>]</span>. Several variants of gradients exist, which differ in their handling of the ReLU nonlinearity. While gradients zero-out the importance signal at ReLUs if the input to the ReLU is negative, deconvolutional networks <span class="citation">[<a href="#ref-voh0OiT2">329</a>]</span> zero-out the importance signal if the signal itself is negative. Guided backpropagation <span class="citation">[<a href="#ref-f2L6isRj">335</a>]</span> combines the two strategies to zero-out the importance signal if either the input to ReLU is negative or the importance signal is negative, in effect discarding negative gradients. However, Mahendran and Vedaldi <span class="citation">[<a href="#ref-3CM9XLyL">336</a>]</span> showed that while guided backpropagation excelled at identifying salient features in the input image, these features showed little class-specificity, producing very similar saliency maps regardless of the class under consideration. Selvaraju et al. <span class="citation">[<a href="#ref-RZsNSRDS">337</a>]</span> attempted to alleviate this by combining gradients and guided backpropagation in Gradient-weighted Class Activation Mapping. Feature maps in the last convolutional layer were associated with classes using gradients, and the weighted activation of these feature maps was multiplied with the result of guided backpropagation to introduce more class specificity. Note that these approaches still would not highlight features that have saturated their contribution to the output, as the gradients with respect to such features would be zero at the input.</p>
-<p>To address the saturation failure mode, strategies have been developed to consider how the output changes between some reference input and the actual input, where the reference input represents a &quot;null&quot; input that it is informative to measure differences against. Sundararajan et al. <span class="citation">[<a href="#ref-WzFOJBiA">338</a>]</span> integrated the gradients as the input was linearly increased from the reference to its actual value. In their examples, which were on image-like data, they used a reference of all zeros. While the numerical integration adds computational overhead, the method is still more efficient on average than perturbation approaches. Further, by relying only on the gradients, the method is a fully black-box approach that is guaranteed to give the same answer for functionally equivalent networks. Shrikumar et al. <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>]</span> developed DeepLIFT, a strategy that used the difference between a neuron's activation on the reference input compared to its activation on the actual input to improve the backpropagation of importance scores. DeepLIFT is a white box method that requires knowledge of the network architecture, but it is more computationally efficient than integrated gradients. Lundberg and Lee <span class="citation">[<a href="#ref-DeOI1oGf">339</a>]</span> noted that several importance scoring methods, including DeepLIFT, integrated gradients and LIME, could all be considered approximations to the Shapely values <span class="citation">[<a href="#ref-YBJdA6LJ">340</a>]</span>, which have a long history in game theory for assigning contributions to players in cooperative games. Briefly, the Shapely values measure the average marginal benefit of including a player over all possible orders in which the players could be included. In the case of DeepLIFT, the &quot;inclusion&quot; of a player is analogous to setting a particular input to its actual value rather than its reference value. DeepLIFT introduced a modification that treated positive and negative contributions separately to address some failure cases of integrated gradients. The modification can be understood as an improved approximation of the Shapely values.</p>
+<p>Pertubration-based approaches change parts of the input and observe the impact on the output of the network. Alipanahi et al. <span class="citation">[<a href="#ref-jJHZHWrl">161</a>]</span> and Zhou &amp; Troyanskaya <span class="citation">[<a href="#ref-2UI1BZuD">165</a>]</span> scored genomic sequences by introducing virtual mutations at individual positions in the sequence and quantifying the change in the output. Umarov et al. <span class="citation">[<a href="#ref-as2HfoSh">175</a>]</span> used a similar strategy, but with sliding windows where the sequence within each sliding window was substituted with a random sequence. Kelley et al. <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span> inserted known protein-binding motifs into the centers of sequences and assessed the change in predicted accessibility. Ribeiro et al. <span class="citation">[<a href="#ref-QwXSJhr0">328</a>]</span> introduced LIME, which constructs a linear model to locally approximate the output of the network on perturbed versions of the input and assigns importance scores accordingly. For analyzing images, Zeiler and Fergus <span class="citation">[<a href="#ref-voh0OiT2">329</a>]</span> applied constant-value masks to different input patches. More recently, marginalizing over the plausible values of an input has been suggested as a way to more accurately estimate contributions <span class="citation">[<a href="#ref-Kk20paR7">330</a>]</span>.</p>
+<p>A common drawback to perturbation-based approaches is computational efficiency: each perturbed version of an input requires a separate forward propagation through the network to compute the output. As noted by Shrikumar et al. <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>]</span>, such methods may also underestimate the impact of features that have saturated their contribution to the output, as can happen when multiple redundant features are present. To reduce the computational overhead of perturbation-based approaches, Fong and Vedaldi <span class="citation">[<a href="#ref-y4t9EzPn">331</a>]</span> solve an optimization problem using gradient descent to discover a minimal subset of inputs to perturb in order to decrease the predicted probability of a selected class. Their method converges in many fewer iterations but requires the perturbation to have a differentiable form.</p>
+<p>Backpropagation-based methods, in which the signal from a target output neuron is propagated backwards to the input layer, are another way to interpret deep networks that sidestep inefficiencies of the perturbastion-basd methods. A classic example of this is calculating the gradients of the output with respect to the input <span class="citation">[<a href="#ref-1YcKYTvO">332</a>]</span> to compute a &quot;saliency map&quot;. Bach et al. <span class="citation">[<a href="#ref-au5CLIOH">333</a>]</span> proposed a strategy called Layerwise Relevance Propagation, which was shown to be equivalent to the element-wise product of the gradient and input <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>,<a href="#ref-b1sc0cgP">334</a>]</span>. Networks with Rectified Linear Units (ReLUs) create nonlinearities that must be addressed. Several variants exist for handling this <span class="citation">[<a href="#ref-voh0OiT2">329</a>,<a href="#ref-f2L6isRj">335</a>]</span>. Backpropagation-based methods are a highly active area of research. Researchers are still actively identifying weaknesses <span class="citation">[<a href="#ref-3CM9XLyL">336</a>]</span>, and new methods are being developed to address them <span class="citation">[<a href="#ref-zhmq9ktJ">166</a>,<a href="#ref-RZsNSRDS">337</a>,<a href="#ref-WzFOJBiA">338</a>]</span>. Lundberg and Lee <span class="citation">[<a href="#ref-DeOI1oGf">339</a>]</span> noted that several importance scoring methods including integrated gradients and LIME could all be considered approximations to Shapely values <span class="citation">[<a href="#ref-YBJdA6LJ">340</a>]</span>, which have a long history in game theory for assigning contributions to players in cooperative games.</p>
 <h4 id="matching-or-exaggerating-the-hidden-representation">Matching or exaggerating the hidden representation</h4>
 <p>Another approach to understanding the network's predictions is to find artificial inputs that produce similar hidden representations to a chosen example. This can elucidate the features that the network uses for prediction and drop the features that the network is insensitive to. In the context of natural images, Mahendran and Vedaldi <span class="citation">[<a href="#ref-19mGl6pfy">341</a>]</span> introduced the &quot;inversion&quot; visualization, which uses gradient descent and backpropagation to reconstruct the input from its hidden representation. The method required placing a prior on the input to favor results that resemble natural images. For genomic sequence, Finnegan and Song <span class="citation">[<a href="#ref-VjsZbMSz">342</a>]</span> used a Markov chain Monte Carlo algorithm to find the maximum-entropy distribution of inputs that produced a similar hidden representation to the chosen input.</p>
 <p>A related idea is &quot;caricaturization&quot;, where an initial image is altered to exaggerate patterns that the network searches for <span class="citation">[<a href="#ref-1FkT6C6oa">343</a>]</span>. This is done by maximizing the response of neurons that are active in the network, subject to some regularizing constraints. Mordvintsev et al. <span class="citation">[<a href="#ref-XLHInhc1">344</a>]</span> leveraged caricaturization to generate aesthetically pleasing images using neural networks.</p>
 <h4 id="activation-maximization">Activation maximization</h4>
-<p>Activation maximization can reveal patterns detected by an individual neuron in the network by generating images which maximally activate that neuron, subject to some regularizing constraints. This technique was first introduced in Ehran et al. <span class="citation">[<a href="#ref-UAAd9Uez">345</a>]</span> and applied in Simonyan et al. <span class="citation">[<a href="#ref-1YcKYTvO">332</a>]</span>, Mordvintsev et al. <span class="citation">[<a href="#ref-XLHInhc1">344</a>]</span>, Yosinksi et al. <span class="citation">[<a href="#ref-17i18PMkR">346</a>]</span>, and Mahendran and Vedaldi <span class="citation">[<a href="#ref-1FkT6C6oa">343</a>]</span>. Lanchantin et al. <span class="citation">[<a href="#ref-Dwi2eAvT">162</a>]</span> applied activation maximization to genomic sequence data. One drawback of this approach is that neural networks often learn highly distributed representations where several neurons cooperatively describe a pattern of interest. Thus, visualizing patterns learned by individual neurons may not always be informative.</p>
+<p>Activation maximization can reveal patterns detected by an individual neuron in the network by generating images which maximally activate that neuron, subject to some regularizing constraints. This technique was first introduced in Ehran et al. <span class="citation">[<a href="#ref-UAAd9Uez">345</a>]</span> and applied in subsequent work <span class="citation">[<a href="#ref-1YcKYTvO">332</a>,<a href="#ref-1FkT6C6oa">343</a>,<a href="#ref-XLHInhc1">344</a>,<a href="#ref-17i18PMkR">346</a>]</span>. Lanchantin et al. <span class="citation">[<a href="#ref-Dwi2eAvT">162</a>]</span> applied activation maximization to genomic sequence data. One drawback of this approach is that neural networks often learn highly distributed representations where several neurons cooperatively describe a pattern of interest. Thus, visualizing patterns learned by individual neurons may not always be informative.</p>
 <h4 id="rnn-specific-approaches">RNN-specific approaches</h4>
-<p>Several interpretation methods are specifically tailored to recurrent neural network architectures. A few key approaches are summarized below.</p>
-<p>The most common form of interpretability provided by RNNs is through attention mechanisms, which have been used in diverse problems such as image captioning and machine translation to select portions of the input to focus on generating a particular output <span class="citation">[<a href="#ref-haHzVaaz">347</a>,<a href="#ref-yHn4SDRI">348</a>]</span>. Deming et al. <span class="citation">[<a href="#ref-SAvEOARL">349</a>]</span> applied the attention mechanism to models trained on genomic sequence. Attention mechanisms provide insight into the model's decision-making process by revealing which portions of the input are used by different outputs. In the clinical domain, Choi et al. <span class="citation">[<a href="#ref-UcRbawKo">350</a>]</span> leveraged attention mechanisms to highlight which aspects of a patient's medical history were most relevant for making diagnoses. Choi et al. <span class="citation">[<a href="#ref-10nDTiETi">351</a>]</span> later extended this work to take into account the structure of disease ontologies and found that the concepts represented by the model aligned with medical knowledge. Note that interpretation strategies that rely on an attention mechanism do not provide insight into the internal logic used by the attention layer to decide which inputs to attend to.</p>
+<p>Several interpretation methods are specifically tailored to recurrent neural network architectures. The most common form of interpretability provided by RNNs is through attention mechanisms, which have been used in diverse problems such as image captioning and machine translation to select portions of the input to focus on generating a particular output <span class="citation">[<a href="#ref-haHzVaaz">347</a>,<a href="#ref-yHn4SDRI">348</a>]</span>. Deming et al. <span class="citation">[<a href="#ref-SAvEOARL">349</a>]</span> applied the attention mechanism to models trained on genomic sequence. Attention mechanisms provide insight into the model's decision-making process by revealing which portions of the input are used by different outputs. In the clinical domain, Choi et al. <span class="citation">[<a href="#ref-UcRbawKo">350</a>]</span> leveraged attention mechanisms to highlight which aspects of a patient's medical history were most relevant for making diagnoses. Choi et al. <span class="citation">[<a href="#ref-10nDTiETi">351</a>]</span> later extended this work to take into account the structure of disease ontologies and found that the concepts represented by the model aligned with medical knowledge. Note that interpretation strategies that rely on an attention mechanism do not provide insight into the logic used by the attention layer.</p>
 <p>Visualizing the activation patterns of the hidden state of a recurrent neural network can also be instructive. Early work by Ghosh and Karamcheti <span class="citation">[<a href="#ref-oc44yBj0">352</a>]</span> used cluster analysis to study hidden states of comparatively small networks trained to recognize strings from a finite state machine. More recently, Karpathy et al. <span class="citation">[<a href="#ref-2cpYveR4">353</a>]</span> showed the existence of individual cells in LSTMs that kept track of quotes and brackets in character-level language models. To facilitate such analyses, LSTMVis <span class="citation">[<a href="#ref-1Ad3UOefc">354</a>]</span> allows interactive exploration of the hidden state of LSTMs on different inputs.</p>
 <p>Another strategy, adopted by Lanchatin et al. <span class="citation">[<a href="#ref-Dwi2eAvT">162</a>]</span> looks at how the output of a recurrent neural network changes as longer and longer subsequences are supplied as input to the network, where the subsequences begin with just the first position and end with the entire sequence. In a binary classification task, this can identify those positions which are responsible for flipping the output of the network from negative to positive. If the RNN is bidirectional, the same process can be repeated on the reverse sequence. As noted by the authors, this approach was less effective at identifying motifs compared to the gradient-based backpropagation approach of Simonyan et al. <span class="citation">[<a href="#ref-1YcKYTvO">332</a>]</span>, illustrating the need for more sophisticated strategies to assign importance scores in recurrent neural networks.</p>
 <p>Murdoch and Szlam <span class="citation">[<a href="#ref-10ViHstXn">355</a>]</span> showed that the output of an LSTM can be decomposed into a product of factors, where each factor can be interpreted as the contribution at a particular timestep. The contribution scores were then used to identify key phrases from a model trained for sentiment analysis and obtained superior results compared to scores derived via a gradient-based approach.</p>
 <h4 id="miscellaneous-approaches">Miscellaneous approaches</h4>
 <p>Toward quantifying the uncertainty of predictions, there has been a renewed interest in confidence intervals for deep neural networks. Early work from Chryssolouris et al. <span class="citation">[<a href="#ref-9SnNyc8Y">356</a>]</span> provided confidence intervals under the assumption of normally-distributed error. A more recent technique known as test-time dropout <span class="citation">[<a href="#ref-1FDihfnM">357</a>]</span> can also be used to obtain a probabilistic interpretation of a network's outputs.</p>
-<p>It can often be informative to understand how the training data affects model learning. Toward this end, Koh and Liang <span class="citation">[<a href="#ref-69wxD9y">358</a>]</span> used influence functions, a technique from robust statistics, to trace a model's predictions back through the learning algorithm to identify the datapoints in the training set that had the most impact on a given prediction. A more free-form approach to interpretability is to visualize the activation patterns of the network on individual inputs and on subsets of the data. ActiVis and CNNvis <span class="citation">[<a href="#ref-QphVo2P2">359</a>,<a href="#ref-AEc66xxR">360</a>]</span> are two frameworks that enable interactive visualization and exploration of large-scale deep learning models.</p>
-<p>An orthogonal strategy is to use a knowledge distillation approach to replace a deep learning model with a more interpretable model that achieves comparable performance. Towards this end, Che et al. <span class="citation">[<a href="#ref-14DAmZTDg">361</a>]</span> used gradient boosted trees to learn interpretable healthcare features from trained deep models.</p>
+<p>It can often be informative to understand how the training data affects model learning. Toward this end, Koh and Liang <span class="citation">[<a href="#ref-69wxD9y">358</a>]</span> used influence functions, a technique from robust statistics, to trace a model's predictions back through the learning algorithm to identify the datapoints in the training set that had the most impact on a given prediction. A more free-form approach to interpretability is to visualize the activation patterns of the network on individual inputs and on subsets of the data. ActiVis and CNNvis <span class="citation">[<a href="#ref-QphVo2P2">359</a>,<a href="#ref-AEc66xxR">360</a>]</span> are two frameworks that enable interactive visualization and exploration of large-scale deep learning models. An orthogonal strategy is to use a knowledge distillation approach to replace a deep learning model with a more interpretable model that achieves comparable performance. Towards this end, Che et al. <span class="citation">[<a href="#ref-14DAmZTDg">361</a>]</span> used gradient boosted trees to learn interpretable healthcare features from trained deep models.</p>
 <p>Finally, it is sometimes possible to train the model to provide justifications for its predictions. Lei et al. <span class="citation">[<a href="#ref-ZUCVI5eU">362</a>]</span> used a generator to identify &quot;rationales&quot;, which are short and coherent pieces of the input text that produce similar results to the whole input when passed through an encoder. The authors applied their approach to a sentiment analysis task and obtained substantially superior results compared to an attention-based method.</p>
 <h4 id="future-outlook">Future outlook</h4>
-<p>While deep learning certainly lags behind most Bayesian models in terms of interpretability, one can safely argue that the interpretability of deep learning is comparable to or exceeds that of many other widely-used machine learning methods such as random forests or SVMs. While it is possible to obtain importance scores for different inputs in a random forest, the same is true for deep learning. Similarly, SVMs trained with a nonlinear kernel are not easily interpretable because the use of the kernel means that one does not obtain an explicit weight matrix. Finally, it is worth noting that some simple machine learning methods are less interpretable in practice than one might expect. For example, a linear model trained on heavily engineered features might be difficult to interpret as the input features themselves are difficult to interpret. Similarly, a decision tree with many nodes and branches may also be difficult for a human to make sense of.</p>
+<p>While deep learning certainly lags behind most Bayesian models in terms of interpretability, one can safely argue that the interpretability of deep learning is comparable to or exceeds that of many other widely-used machine learning methods such as random forests or SVMs. While it is possible to obtain importance scores for different inputs in a random forest, the same is true for deep learning. Similarly, SVMs trained with a nonlinear kernel are not easily interpretable because the use of the kernel means that one does not obtain an explicit weight matrix. Finally, it is worth noting that some simple machine learning methods are less interpretable in practice than one might expect. A linear model trained on heavily engineered features might be difficult to interpret as the input features themselves are difficult to interpret. Similarly, a decision tree with many nodes and branches may also be difficult for a human to make sense of.</p>
 <p>There are several directions that might benefit the development of interpretability techniques. The first is the introduction of gold standard benchmarks that different interpretability approaches could be compared against, similar in spirit to how datasets like ImageNet and CIFAR spurred the development of deep learning for computer vision. It would also be helpful if the community placed more emphasis on domains outside of computer vision. Computer vision is often used as the example application of interpretability methods, but it is arguably not the domain with the most pressing need. Finally, closer integration of interpretability approaches with popular deep learning frameworks would make it easier for practitioners to apply and experiment with different approaches to understanding their deep learning models.</p>
 <h3 id="data-limitations">Data limitations</h3>
-<p>A lack of large-scale, high-quality, correctly labeled training data has impacted deep learning in nearly all applications we have discussed, from healthcare to genomics to drug discovery. The challenges of training complex, high-parameter neural networks from few examples are obvious, but uncertainty in the labels of those examples can be just as problematic. For example, in genomics, labeled data may be derived from an experimental assay with known and unknown technical artifacts, biases, and error profiles. It is possible to weight training examples or construct Bayesian models to account for uncertainty or non-independence in the data, as described in the TF binding example above. As another example, Park et al. <span class="citation">[<a href="#ref-5tvnB4uW">363</a>]</span> estimated shared non-biological signal between datasets to correct for non-independence related to assay platform or other factors in a Bayesian integration of many datasets. However, such techniques are rarely placed front and center in any description of methods and may be easily overlooked.</p>
+<p>A lack of large-scale, high-quality, correctly labeled training data has impacted deep learning in nearly all applications we have discussed, from healthcare to genomics to drug discovery. The challenges of training complex, high-parameter neural networks from few examples are obvious, but uncertainty in the labels of those examples can be just as problematic. In genomics labeled data may be derived from an experimental assay with known and unknown technical artifacts, biases, and error profiles. It is possible to weight training examples or construct Bayesian models to account for uncertainty or non-independence in the data, as described in the TF binding example above. As another example, Park et al. <span class="citation">[<a href="#ref-5tvnB4uW">363</a>]</span> estimated shared non-biological signal between datasets to correct for non-independence related to assay platform or other factors in a Bayesian integration of many datasets. However, such techniques are rarely placed front and center in any description of methods and may be easily overlooked.</p>
 <p>For some types of data, especially images, it is straightforward to augment training datasets by splitting a single labeled example into multiple examples. For example, an image can easily be rotated, flipped, or translated and retain its label <span class="citation">[<a href="#ref-9G9Hv1Pp">59</a>]</span>. 3D MRI and 4D fMRI (with time as a dimension) data can be decomposed into sets of 2D images <span class="citation">[<a href="#ref-11NHbWB1V">364</a>]</span>. This can greatly expand the number of training examples but artificially treats such derived images as independent instances and sacrifices the structure inherent in the data. CellCnn trains a model to recognize rare cell populations in single-cell data by creating training instances that consist of subsets of cells that are randomly sampled with replacement from the full dataset <span class="citation">[<a href="#ref-r3Gbjksq">217</a>]</span>.</p>
-<p>Simulated or semi-synthetic training data has been employed in multiple biomedical domains, though many of these ideas are not specific to deep learning. Training and evaluating on simulated data, for instance, generating synthetic TF binding sites with position weight matrices <span class="citation">[<a href="#ref-iEmvzeT8">164</a>]</span> or RNA-seq reads for predicting mRNA transcript boundaries <span class="citation">[<a href="#ref-2M3zXijc">365</a>]</span>, is a standard practice in bioinformatics. This strategy can help benchmark algorithms when the available gold standard dataset are imperfect, but it should be paired with an evaluation on real data, as in the prior examples <span class="citation">[<a href="#ref-iEmvzeT8">164</a>,<a href="#ref-2M3zXijc">365</a>]</span>. In rare cases, models trained on simulated data have been successfully applied directly to real data <span class="citation">[<a href="#ref-2M3zXijc">365</a>]</span>.</p>
-<p>Simulated data can also be create negative examples when only positive training instances are available. DANN <span class="citation">[<a href="#ref-15E5yG1Ho">33</a>]</span> adopts this approach to predict the pathogenicity of genetic variants using semi-synthetic training data from Combined Annotation-Dependent Depletion <span class="citation">[<a href="#ref-KxEzGxJ6">366</a>]</span>. Though our emphasis here is on the training strategy, it should be noted that logistic regression outperformed DANN when distinguishing known pathogenic mutations from likely benign variants in real data. Similarly, a somatic mutation caller has been trained by injecting mutations into real sequencing datasets <span class="citation">[<a href="#ref-ECTm1SuA">257</a>]</span>. This method was successful at detecting mutations in other semi-synthetic datasets but was not validated on real data.</p>
+<p>Simulated or semi-synthetic training data has been employed in multiple biomedical domains, though many of these ideas are not specific to deep learning. Training and evaluating on simulated data, for instance, generating synthetic TF binding sites with position weight matrices <span class="citation">[<a href="#ref-iEmvzeT8">164</a>]</span> or RNA-seq reads for predicting mRNA transcript boundaries <span class="citation">[<a href="#ref-2M3zXijc">365</a>]</span>, is a standard practice in bioinformatics. This strategy can help benchmark algorithms when the available gold standard dataset is imperfect, but it should be paired with an evaluation on real data, as in the prior examples <span class="citation">[<a href="#ref-iEmvzeT8">164</a>,<a href="#ref-2M3zXijc">365</a>]</span>. In rare cases, models trained on simulated data have been successfully applied directly to real data <span class="citation">[<a href="#ref-2M3zXijc">365</a>]</span>.</p>
+<p>Data can be simulated to create negative examples when only positive training instances are available. DANN <span class="citation">[<a href="#ref-15E5yG1Ho">33</a>]</span> adopts this approach to predict the pathogenicity of genetic variants using semi-synthetic training data from Combined Annotation-Dependent Depletion <span class="citation">[<a href="#ref-KxEzGxJ6">366</a>]</span>. Though our emphasis here is on the training strategy, it should be noted that logistic regression outperformed DANN when distinguishing known pathogenic mutations from likely benign variants in real data. Similarly, a somatic mutation caller has been trained by injecting mutations into real sequencing datasets <span class="citation">[<a href="#ref-ECTm1SuA">257</a>]</span>. This method detected mutations in other semi-synthetic datasets but was not validated on real data.</p>
 <p>In settings where the experimental observations are biased toward positive instances, such as MHC protein and peptide ligand binding affinity <span class="citation">[<a href="#ref-1Hk3NTSn2">367</a>]</span>, or the negative instances vastly outnumber the positives, such as high-throughput chemical screening <span class="citation">[<a href="#ref-1E0x7QgLP">303</a>]</span>, training datasets have been augmented by adding additional instances and assuming they are negative. There is some evidence that this can improve performance <span class="citation">[<a href="#ref-1E0x7QgLP">303</a>]</span>, but in other cases it was only beneficial when the real training datasets were extremely small <span class="citation">[<a href="#ref-1Hk3NTSn2">367</a>]</span>. Overall, training with simulated and semi-simulated data is a valuable idea for overcoming limited sample sizes but one that requires more rigorous evaluation on real ground-truth datasets before we can recommend it for widespread use. There is a risk that a model will easily discriminate synthetic examples but not generalize to real data.</p>
-<p>Multimodal, multi-task, and transfer learning, discussed in detail below, can also combat data limitations to some degree. There are also emerging network architectures, such as Diet Networks for high-dimensional SNP data <span class="citation">[<a href="#ref-15JUKBg9y">368</a>]</span>. These use multiple networks to drastically reduce the number of free parameters by first flipping the problem and training a network to predict parameters (weights) for each input (SNP) to learn a feature embedding. This embedding (e.g. from principal component analysis, per class histograms, or a Word2vec <span class="citation">[<a href="#ref-1GhHIDxuW">76</a>]</span> generalization) can be learned directly from input data or take advantage of other datasets or domain knowledge. Additionally, in this task the features are the examples, an important advantage when it is typical to have 500 thousand or more SNPs and only a few thousand patients. Finally, this embedding is of a much lower dimension, allowing for a large reduction in the number of free parameters. In the example given, the number of free parameters from was reduced from 30 million to 50 thousand, a factor of 600.</p>
+<p>Multimodal, multi-task, and transfer learning, discussed in detail below, can also combat data limitations to some degree. There are also emerging network architectures, such as Diet Networks for high-dimensional SNP data <span class="citation">[<a href="#ref-15JUKBg9y">368</a>]</span>. These use multiple networks to drastically reduce the number of free parameters by first flipping the problem and training a network to predict parameters (weights) for each input (SNP) to learn a feature embedding. This embedding (e.g. from principal component analysis, per class histograms, or a Word2vec <span class="citation">[<a href="#ref-1GhHIDxuW">76</a>]</span> generalization) can be learned directly from input data or take advantage of other datasets or domain knowledge. Additionally, in this task the features are the examples, an important advantage when it is typical to have 500 thousand or more SNPs and only a few thousand patients. Finally, this embedding is of a much lower dimension, allowing for a large reduction in the number of free parameters. In the example given, the number of free parameters was reduced from 30 million to 50 thousand, a factor of 600.</p>
 <h3 id="hardware-limitations-and-scaling">Hardware limitations and scaling</h3>
 <p>Efficiently scaling deep learning is challenging, and there is a high computational cost (e.g. time, memory, and energy) associated with training neural networks and using them to make predictions. This is one of the reasons why neural networks have only recently found widespread use <span class="citation">[<a href="#ref-BQS8ClV0">369</a>]</span>.</p>
-<p>Many have sought to curb these costs, with methods ranging from the very applied (e.g. reduced numerical precision <span class="citation">[<a href="#ref-CKcJuj03">370</a>–<a href="#ref-1GUizyE8e">373</a>]</span>) to the exotic and theoretic (e.g. training small networks to mimic large networks and ensembles <span class="citation">[<a href="#ref-1AhGoHZP9">326</a>,<a href="#ref-1CRF3gAV">374</a>]</span>). The largest gains in efficiency have come from computation with graphics processing units (GPUs) <span class="citation">[<a href="#ref-BQS8ClV0">369</a>,<a href="#ref-F3e4wfzQ">375</a>–<a href="#ref-1FocAi7N0">379</a>]</span>, which excel at the matrix and vector operations so central to deep learning. The massively parallel nature of GPUs allows additional optimizations, such as accelerated mini-batch gradient descent <span class="citation">[<a href="#ref-NSgduYNT">376</a>,<a href="#ref-IULiPa6L">377</a>,<a href="#ref-aClNvbyM">380</a>,<a href="#ref-fNkl8HFz">381</a>]</span>. However, GPUs also have a limited quantity of memory, making it difficult to implement networks of useful size and complexity on a single GPU or machine <span class="citation">[<a href="#ref-CCS5KSIM">68</a>,<a href="#ref-F3e4wfzQ">375</a>]</span>. This restriction has sometimes forced computational biologists to use workarounds or limit the size of an analysis. For example, Chen et al. <span class="citation">[<a href="#ref-12QQw9p7v">143</a>]</span> aimed to infer the expression level of all genes with a single neural network, but due to memory restrictions they randomly partitioned genes into two halves and analyzed each separately. In other cases, researchers limited the size of their neural network <span class="citation">[<a href="#ref-BhfjKSY3">27</a>]</span> or the total number of training instances <span class="citation">[<a href="#ref-2dU8f4XJ">311</a>]</span>. Some have also chosen to use CPU implementations rather than sacrifice network size or performance <span class="citation">[<a href="#ref-x0M6vals">382</a>]</span>.</p>
-<p>While steady improvements in GPU hardware may alleviate this issue, it is unclear whether advances can occur quickly enough to keep up with the growing amount of available biological data or increasing network complexity. Much has been done to minimize the memory requirements of neural networks <span class="citation">[<a href="#ref-1AhGoHZP9">326</a>,<a href="#ref-CKcJuj03">370</a>–<a href="#ref-1GUizyE8e">373</a>,<a href="#ref-YwdqeYZi">383</a>,<a href="#ref-15lYGmZpY">384</a>]</span>, but there is also growing interest in specialized hardware, such as field-programmable gate arrays (FPGAs) <span class="citation">[<a href="#ref-1FocAi7N0">379</a>,<a href="#ref-9NKsJjSw">385</a>]</span> and application-specific integrated circuits (ASICs) <span class="citation">[<a href="#ref-ULagTifF">386</a>]</span>. Specialized hardware promises improvements in deep learning at reduced time, energy, and memory <span class="citation">[<a href="#ref-1FocAi7N0">379</a>]</span>. Obviously, there is less software available for such highly specialized hardware <span class="citation">[<a href="#ref-9NKsJjSw">385</a>]</span>, and it could be a difficult investment for those not solely interested in deep learning. However, it is likely that such options will find increased support as they become a more popular platform for deep learning and general computation.</p>
+<p>Many have sought to curb these costs, with methods ranging from the very applied (e.g. reduced numerical precision <span class="citation">[<a href="#ref-CKcJuj03">370</a>–<a href="#ref-1GUizyE8e">373</a>]</span>) to the exotic and theoretic (e.g. training small networks to mimic large networks and ensembles <span class="citation">[<a href="#ref-1AhGoHZP9">326</a>,<a href="#ref-1CRF3gAV">374</a>]</span>). The largest gains in efficiency have come from computation with graphics processing units (GPUs) <span class="citation">[<a href="#ref-BQS8ClV0">369</a>,<a href="#ref-F3e4wfzQ">375</a>–<a href="#ref-1FocAi7N0">379</a>]</span>, which excel at the matrix and vector operations so central to deep learning. The massively parallel nature of GPUs allows additional optimizations, such as accelerated mini-batch gradient descent <span class="citation">[<a href="#ref-NSgduYNT">376</a>,<a href="#ref-IULiPa6L">377</a>,<a href="#ref-aClNvbyM">380</a>,<a href="#ref-fNkl8HFz">381</a>]</span>. However, GPUs also have limited memory, making networks of useful size and complexity difficult to implement on a single GPU or machine <span class="citation">[<a href="#ref-CCS5KSIM">68</a>,<a href="#ref-F3e4wfzQ">375</a>]</span>. This restriction has sometimes forced computational biologists to use workarounds or limit the size of an analysis. Chen et al. <span class="citation">[<a href="#ref-12QQw9p7v">143</a>]</span> inferred the expression level of all genes with a single neural network, but due to memory restrictions they randomly partitioned genes into two separately analyzed halves. In other cases, researchers limited the size of their neural network <span class="citation">[<a href="#ref-BhfjKSY3">27</a>]</span> or the total number of training instances <span class="citation">[<a href="#ref-2dU8f4XJ">311</a>]</span>. Some have also chosen to use CPU implementations rather than sacrifice network size or performance <span class="citation">[<a href="#ref-x0M6vals">382</a>]</span>.</p>
+<p>While steady improvements in GPU hardware may alleviate this issue, it is unclear whether advances will occur quickly enough to keep pace with the growing biological datasets and increasingly complex neural networks. Much has been done to minimize the memory requirements of neural networks <span class="citation">[<a href="#ref-1AhGoHZP9">326</a>,<a href="#ref-CKcJuj03">370</a>–<a href="#ref-1GUizyE8e">373</a>,<a href="#ref-YwdqeYZi">383</a>,<a href="#ref-15lYGmZpY">384</a>]</span>, but there is also growing interest in specialized hardware, such as field-programmable gate arrays (FPGAs) <span class="citation">[<a href="#ref-1FocAi7N0">379</a>,<a href="#ref-9NKsJjSw">385</a>]</span> and application-specific integrated circuits (ASICs) <span class="citation">[<a href="#ref-ULagTifF">386</a>]</span>. Less software is available for such highly specialized hardware <span class="citation">[<a href="#ref-9NKsJjSw">385</a>]</span>. But specialized hardware promises improvements in deep learning at reduced time, energy, and memory <span class="citation">[<a href="#ref-1FocAi7N0">379</a>]</span>. Specialized hardware may be a difficult investment for those not solely interested in deep learning, but for those with a deep learning focus these solutions may become popular.</p>
 <p>Distributed computing is a general solution to intense computational requirements and has enabled many large-scale deep learning efforts. Some types of distributed computation <span class="citation">[<a href="#ref-xE3EYmck">387</a>,<a href="#ref-1XcexUAV">388</a>]</span> are not suitable for deep learning <span class="citation">[<a href="#ref-17cBimWgp">389</a>]</span>, but much progress has been made. There now exist a number of algorithms <span class="citation">[<a href="#ref-w6CoVmFK">372</a>,<a href="#ref-17cBimWgp">389</a>,<a href="#ref-HIiQN4bd">390</a>]</span>, tools <span class="citation">[<a href="#ref-rmJZ2Aui">391</a>–<a href="#ref-Gp4OR9Lf">393</a>]</span>, and high-level libraries <span class="citation">[<a href="#ref-FwEK0msb">394</a>,<a href="#ref-y9IoEy4r">395</a>]</span> for deep learning in a distributed environment, and it is possible to train very complex networks with limited infrastructure <span class="citation">[<a href="#ref-4MZ2tmZ8">396</a>]</span>. Besides handling very large networks, distributed or parallelized approaches offer other advantages, such as improved ensembling <span class="citation">[<a href="#ref-JUF9VoRD">397</a>]</span> or accelerated hyperparameter optimization <span class="citation">[<a href="#ref-wz83yfHF">398</a>,<a href="#ref-1FSwIjR9s">399</a>]</span>.</p>
 <p>Cloud computing, which has already seen wide adoption in genomics <span class="citation">[<a href="#ref-B6g0qKf4">400</a>]</span>, could facilitate easier sharing of the large datasets common to biology <span class="citation">[<a href="#ref-1E7bFCRV4">401</a>,<a href="#ref-q0SsFrZd">402</a>]</span>, and may be key to scaling deep learning. Cloud computing affords researchers flexibility, and enables the use of specialized hardware (e.g., FPGAs, ASICs, GPUs) without major investment. As such, it could be easier to address the different challenges associated with the multitudinous layers and architectures available <span class="citation">[<a href="#ref-ZSVsnPVO">403</a>]</span>. Though many are reluctant to store sensitive data (e.g. patient electronic health records) in the cloud, secure, regulation-compliant cloud services do exist <span class="citation">[<a href="#ref-ObFN78yp">404</a>]</span>.</p>
 <h3 id="data-code-and-model-sharing">Data, code, and model sharing</h3>
-<p>A robust culture of data, code, and model sharing would do much to speed advances in this domain. The cultural barriers of data sharing in particular are perhaps best captured by the implications of using the term &quot;research parasite&quot; to describe scientists who use data from other researchers <span class="citation">[<a href="#ref-o0F1MXBC">405</a>]</span>. In short, a field that honors only discoveries and not the hard work of generating useful data will have difficulty encouraging scientists to share their hard-won data. Unfortunately, it's precisely those data that would help to power deep learning in the domain. Efforts are underway to recognize those who promote an ecosystem of rigorous sharing and analysis <span class="citation">[<a href="#ref-194IoYUs3">406</a>]</span>.</p>
-<p>The sharing of high-quality, labeled datasets will be especially valuable. In addition, researchers who invest time to preprocess datasets to be suitable for deep learning can make the preprocessing code (e.g. Basset <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span> and variationanalysis <span class="citation">[<a href="#ref-GSLRw2L5">255</a>]</span>) and cleaned data (e.g. MoleculeNet <span class="citation">[<a href="#ref-16OPHvAij">309</a>]</span>) publicly available to catalyze further research. However, there are complex privacy and legal issues involved in sharing patient data that cannot be ignored. Furthermore, in some domains, some of the best training data has been generated privately, for example, high-throughput chemical screening data at pharmaceutical companies. One perspective is that there is little expectation or incentive for this private data to be shared. However, data are not inherently valuable. Instead, the insights that we glean from them are where the value lies. Private companies may establish a competitive advantage by releasing sufficient data for improved methods to be developed.</p>
+<p>A robust culture of data, code, and model sharing would speed advances in this domain. The cultural barriers to data sharing in particular are perhaps best captured by the use of the term &quot;research parasite&quot; to describe scientists who use data from other researchers <span class="citation">[<a href="#ref-o0F1MXBC">405</a>]</span>. A field that honors only discoveries and not the hard work of generating useful data will have difficulty encouraging scientists to share their hard-won data. Unfortunately, it's precisely those data that would help to power deep learning in the domain. Efforts are underway to recognize those who promote an ecosystem of rigorous sharing and analysis <span class="citation">[<a href="#ref-194IoYUs3">406</a>]</span>.</p>
+<p>The sharing of high-quality, labeled datasets will be especially valuable. In addition, researchers who invest time to preprocess datasets to be suitable for deep learning can make the preprocessing code (e.g. Basset <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span> and variationanalysis <span class="citation">[<a href="#ref-GSLRw2L5">255</a>]</span>) and cleaned data (e.g. MoleculeNet <span class="citation">[<a href="#ref-16OPHvAij">309</a>]</span>) publicly available to catalyze further research. However, there are complex privacy and legal issues involved in sharing patient data that cannot be ignored. In some domains high-quality training data has been generated privately, i.e. high-throughput chemical screening data at pharmaceutical companies. One perspective is that there is little expectation or incentive for this private data to be shared. However, data are not inherently valuable. Instead, the insights that we glean from them are where the value lies. Private companies may establish a competitive advantage by releasing data sufficient for improved methods to be developed.</p>
 <p>Code sharing and open source licensing is essential for continued progress in this domain. We strongly advocate following established best practices for sharing source code, archiving code in repositories that generate digital object identifiers, and open licensing <span class="citation">[<a href="#ref-gvyja7v1">407</a>]</span> regardless of the minimal requirements, or lack thereof, set by journals, conferences, or preprint servers. In addition, it is important for authors to share not only code for their core models but also scripts and code used for data cleaning (see above) and hyperparameter optimization. These improve reproducibility and serve as documentation of the detailed decisions that impact model performance but may not be exhaustively captured in a manuscript's methods text.</p>
 <p>Because many deep learning models are often built using one of several popular software frameworks, it is also possible to directly share trained predictive models. The availability of pre-trained models can accelerate research, with image classifiers as an apt example. A pre-trained neural network can be quickly fine-tuned on new data and used in transfer learning, as discussed below. Taking this idea to the extreme, genomic data has been artificially encoded as images in order to benefit from pre-trained image classifiers <span class="citation">[<a href="#ref-FVfZESYP">254</a>]</span>. &quot;Model zoos&quot; -- collections of pre-trained models -- are not yet common in biomedical domains but have started to appear in genomics applications <span class="citation">[<a href="#ref-19EJTHByG">213</a>,<a href="#ref-117PEpTMe">408</a>]</span>. Sharing models for patient data requires great care because deep learning models can be attacked to identify examples used in training. We discuss this issue as well as recent techniques to mitigate these concerns in the patient categorization section.</p>
-<p>DeepChem <span class="citation">[<a href="#ref-P4ixsM8i">308</a>–<a href="#ref-Ytvk62dX">310</a>]</span> and DragoNN <span class="citation">[<a href="#ref-117PEpTMe">408</a>]</span> exemplify the benefits of sharing pre-trained models and code under an open source license. DeepChem, which targets drug discovery and quantum chemistry, has actively encouraged and received community contributions of learning algorithms and benchmarking datasets. As a consequence, it now supports of a large suite of machine learning approaches, both deep learning and competing strategies, that can be run on diverse test cases. This realistic, continual evaluation will play a critical role in assessing which techniques are most promising for chemical screening and drug discovery. Like formal, organized challenges such as the ENCODE-DREAM <em>in vivo</em> Transcription Factor Binding Site Prediction Challenge <span class="citation">[<a href="#ref-wW6QbBXz">409</a>]</span>, DeepChem provides a forum for the fair, critical evaluations that are not always conducted in individual methodological papers, which can be biased toward favoring a new proposed algorithm. Likewise DragoNN (Deep RegulAtory GenOmic Neural Networks), offers not only code and a model zoo but also a detailed tutorial and partner package for simulating training data. These resources, especially the ability to simulate datasets that are sufficiently complex to demonstrate the challenges of training neural networks but small enough to train quickly on a CPU, are important for (human) training and attracting machine learning researchers to problems in genomics and healthcare. We have even included DragoNN and hands-on model training into the curriculum of a graduate student course.</p>
+<p>DeepChem <span class="citation">[<a href="#ref-P4ixsM8i">308</a>–<a href="#ref-Ytvk62dX">310</a>]</span> and DragoNN <span class="citation">[<a href="#ref-117PEpTMe">408</a>]</span> exemplify the benefits of sharing pre-trained models and code under an open source license. DeepChem, which targets drug discovery and quantum chemistry, has actively encouraged and received community contributions of learning algorithms and benchmarking datasets. As a consequence, it now supports a large suite of machine learning approaches, both deep learning and competing strategies, that can be run on diverse test cases. This realistic, continual evaluation will play a critical role in assessing which techniques are most promising for chemical screening and drug discovery. Like formal, organized challenges such as the ENCODE-DREAM <em>in vivo</em> Transcription Factor Binding Site Prediction Challenge <span class="citation">[<a href="#ref-wW6QbBXz">409</a>]</span>, DeepChem provides a forum for the fair, critical evaluations that are not always conducted in individual methodological papers, which can be biased toward favoring a new proposed algorithm. Likewise DragoNN (Deep RegulAtory GenOmic Neural Networks) offers not only code and a model zoo but also a detailed tutorial and partner package for simulating training data. These resources, especially the ability to simulate datasets that are sufficiently complex to demonstrate the challenges of training neural networks but small enough to train quickly on a CPU, are important for training students and attracting machine learning researchers to problems in genomics and healthcare.</p>
 <h3 id="multimodal-multi-task-and-transfer-learning">Multimodal, multi-task, and transfer learning</h3>
-<p>As discussed above, the fact that biomedical datasets often contain a limited number of instances or labels can be a cause of poor performance of machine learning algorithms. When trained on such datasets, deep learning models are particularly prone to overfitting due to their high representational power. However, transfer learning techniques, also known as domain adaptation, enable transfer of extracted patterns between different datasets and even domains. This approach consists of training a model for the base task and subsequently reusing the trained model for the target problem in hand. The first step allows a model to take advantage of a larger amount of data and/or labels to extract better feature representations. Transferring learnt features in deep neural networks improves performance compared to randomly initialized features even when pre-training and target sets are dissimilar. However, transferability of features decreases as the distance between the base task and target task increases <span class="citation">[<a href="#ref-enhj7VT6">410</a>]</span>.</p>
-<p>In image analysis, previous examples of deep transfer learning applications proved large-scale natural image sets <span class="citation">[<a href="#ref-cBVeXnZx">42</a>]</span> to be useful for pre-training models that can then serve as generic feature extractors applied to various types of biological images <span class="citation">[<a href="#ref-irSe12Sm">14</a>,<a href="#ref-BMg062hc">203</a>,<a href="#ref-HlDY7trA">411</a>,<a href="#ref-z3I2IudI">412</a>]</span>. More recently, deep learning models trained to predict protein sub-cellular localization successfully performed predictions for proteins not originally present in the training set <span class="citation">[<a href="#ref-2a7MHtAx">413</a>]</span>. Moreover, in this type of task, learnt features performed reasonably well even when applied to images obtained using different fluorescent labels, imaging techniques, and different cell types <span class="citation">[<a href="#ref-DcnNfASG">414</a>]</span>. However, there are no established theoretical guarantees for feature transferability between distant domains such as natural images and various modalities of biological imaging. Because learnt patterns are represented in deep neural networks in a layer-wise hierarchical fashion, this issue is usually addressed by fixing an empirically chosen number of layers that preserve generic characteristics of both training and target datasets. The model is then fine-tuned by re-training multiple networks' top layers on the specific dataset in order to re-learn domain-specific high level concepts (e.g. fine-tuning for radiology image classification <span class="citation">[<a href="#ref-x6HXFAS4">54</a>]</span>). Fine-tuning on specific biological datasets enables more focused predictions.</p>
+<p>The fact that biomedical datasets often contain a limited number of instances or labels can cause poor performance of deep learning algorithms. These models are particularly prone to overfitting due to their high representational power. However, transfer learning techniques, also known as domain adaptation, enable transfer of extracted patterns between different datasets and even domains. This approach consists of training a model for the base task and subsequently reusing the trained model for the target problem. The first step allows a model to take advantage of a larger amount of data and/or labels to extract better feature representations. Transferring learned features in deep neural networks improves performance compared to randomly initialized features even when pre-training and target sets are dissimilar. However, transferability of features decreases as the distance between the base task and target task increases <span class="citation">[<a href="#ref-enhj7VT6">410</a>]</span>.</p>
+<p>In image analysis, previous examples of deep transfer learning applications proved large-scale natural image sets <span class="citation">[<a href="#ref-cBVeXnZx">42</a>]</span> to be useful for pre-training models that serve as generic feature extractors for various types of biological images <span class="citation">[<a href="#ref-irSe12Sm">14</a>,<a href="#ref-BMg062hc">203</a>,<a href="#ref-HlDY7trA">411</a>,<a href="#ref-z3I2IudI">412</a>]</span>. More recently, deep learning models predicted protein sub-cellular localization for proteins not originally present in a training set <span class="citation">[<a href="#ref-2a7MHtAx">413</a>]</span>. Moreover, learned features performed reasonably well even when applied to images obtained using different fluorescent labels, imaging techniques, and different cell types <span class="citation">[<a href="#ref-DcnNfASG">414</a>]</span>. However, there are no established theoretical guarantees for feature transferability between distant domains such as natural images and various modalities of biological imaging. Because learned patterns are represented in deep neural networks in a layer-wise hierarchical fashion, this issue is usually addressed by fixing an empirically chosen number of layers that preserve generic characteristics of both training and target datasets. The model is then fine-tuned by re-training top layers on the specific dataset in order to re-learn domain-specific high level concepts (e.g. fine-tuning for radiology image classification <span class="citation">[<a href="#ref-x6HXFAS4">54</a>]</span>). Fine-tuning on specific biological datasets enables more focused predictions.</p>
 <p>In genomics, the Basset package <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span> for predicting chromatin accessibility was shown to rapidly learn and accurately predict on new data by leveraging a model pre-trained on available public data. To simulate this scenario, authors put aside 15 of 164 cell type datasets and trained the Basset model on the remaining 149 datasets. Then, they fine-tuned the model with one training pass of each of the remaining datasets and achieved results close to the model trained on all 164 datasets together. In another example, Min et al. <span class="citation">[<a href="#ref-jV2YerUS">176</a>]</span> demonstrated how training on the experimentally validated FANTOM5 permissive enhancer dataset followed by fine-tuning on ENCODE enhancer datasets improved cell type-specific predictions, outperforming state-of-the-art results. In drug design, general RNN models trained to generate molecules from the ChEMBL database have been fine-tuned to produce drug-like compounds for specific targets <span class="citation">[<a href="#ref-8LWFFeYg">320</a>,<a href="#ref-1EayJRsI">323</a>]</span>.</p>
-<p>Related to transfer learning, multimodal learning assumes simultaneous learning from various types of inputs, such as images and text. It can capture features that describe common concepts across input modalities. Generative graphical models like restricted Boltzmann machines (RBM) and their stacked versions, deep Boltzmann machines (DBM), and deep belief networks (DBN), demonstrate successful extraction of more informative features for one modality (images or video) when jointly learnt with other modalities (audio or text) <span class="citation">[<a href="#ref-1eN66lwn">415</a>]</span>. Deep graphical models such as DBNs are considered to be well-suited for multimodal learning tasks because they learn a joint probability distribution from inputs. They can be pre-trained in an unsupervised fashion on large unlabeled data and then fine-tuned on a smaller number of labeled examples. When labels are available, convolutional neural networks are ubiquitously used because they can be trained end-to-end with backpropagation and demonstrate state-of-the-art performance in many discriminative tasks <span class="citation">[<a href="#ref-irSe12Sm">14</a>]</span>.</p>
-<p>Jha et al. <span class="citation">[<a href="#ref-N0HBi8MH">151</a>]</span> showed that an integrated training approach delivers better performance compared to individual networks. They compared a number of feed-forward architectures trained on RNA-seq data with and without an additional set of CLIP-seq, knockdown, and over-expression based input features. Results showed that the integrative deep model generalized well for combined data, offering large performance improvement for alternative splicing event estimation. Chaudhary et al. <span class="citation">[<a href="#ref-obeRVckH">416</a>]</span> trained a deep autoencoder model jointly on RNA-seq, miRNA-seq, and methylation data from The Cancer Genome Atlas to predict survival subgroups of hepatocellular carcinoma patients. This multimodal approach that treated different omic data types as different modalities outperformed both traditional methods (PCA) and single-omic models. Interestingly, multi-omic model performance did not improve when combined with clinical information, suggesting that the model was able to capture redundant contributions of clinical features through their correlated genomic features. Chen et al. <span class="citation">[<a href="#ref-rmjDc5rm">138</a>]</span> used deep belief networks to learn phosphorylation states of a common set of signaling proteins in primary cultured bronchial cells collected from rats and humans treated with distinct stimuli. By interpreting species as different modalities representing similar high-level concepts, they showed that DBNs were able to capture cross-species representation of signaling mechanisms in response to a common stimuli. Another application used DBNs for joint unsupervised feature learning from cancer datasets containing gene expression, DNA methylation, and miRNA expression data <span class="citation">[<a href="#ref-1EtavGKI4">145</a>]</span>. This approach allowed for the capture of intrinsic relationships in different modalities and for better clustering performance over conventional k-means.</p>
+<p>Related to transfer learning, multimodal learning assumes simultaneous learning from various types of inputs, such as images and text. It can capture features that describe common concepts across input modalities. Generative graphical models like RBMs and their stacked versions, deep Boltzmann machines (DBM), and deep belief networks (DBN), demonstrate successful extraction of more informative features for one modality (images or video) when jointly learned with other modalities (audio or text) <span class="citation">[<a href="#ref-1eN66lwn">415</a>]</span>. Deep graphical models such as DBNs are considered to be well-suited for multimodal learning tasks because they learn a joint probability distribution from inputs. They can be pre-trained in an unsupervised fashion on large unlabeled data and then fine-tuned on a smaller number of labeled examples. When labels are available, convolutional neural networks are ubiquitously used because they can be trained end-to-end with backpropagation and demonstrate state-of-the-art performance in many discriminative tasks <span class="citation">[<a href="#ref-irSe12Sm">14</a>]</span>.</p>
+<p>Jha et al. <span class="citation">[<a href="#ref-N0HBi8MH">151</a>]</span> showed that integrated training delivered better performance than individual networks. They compared a number of feed-forward architectures trained on RNA-seq data with and without an additional set of CLIP-seq, knockdown, and over-expression based input features. The integrative deep model generalized well for combined data, offering a large performance improvement for alternative splicing event estimation. Chaudhary et al. <span class="citation">[<a href="#ref-obeRVckH">416</a>]</span> trained a deep autoencoder model jointly on RNA-seq, miRNA-seq, and methylation data from The Cancer Genome Atlas to predict survival subgroups of hepatocellular carcinoma patients. This multimodal approach that treated different omic data types as different modalities outperformed both traditional methods (PCA) and single-omic models. Interestingly, multi-omic model performance did not improve when combined with clinical information, suggesting that the model was able to capture redundant contributions of clinical features through their correlated genomic features. Chen et al. <span class="citation">[<a href="#ref-rmjDc5rm">138</a>]</span> used deep belief networks to learn phosphorylation states of a common set of signaling proteins in primary cultured bronchial cells collected from rats and humans treated with distinct stimuli. By interpreting species as different modalities representing similar high-level concepts, they showed that DBNs were able to capture cross-species representation of signaling mechanisms in response to a common stimuli. Another application used DBNs for joint unsupervised feature learning from cancer datasets containing gene expression, DNA methylation, and miRNA expression data <span class="citation">[<a href="#ref-1EtavGKI4">145</a>]</span>. This approach allowed for the capture of intrinsic relationships in different modalities and for better clustering performance over conventional k-means.</p>
 <p>Multimodal learning with CNNs is usually implemented as a collection of individual networks in which each learns representations from single data type. These individual representations are further concatenated before or within fully-connected layers. FIDDLE <span class="citation">[<a href="#ref-yOz8Ybj2">417</a>]</span> is an example of a multimodal CNN that represents an ensemble of individual networks that take NET-seq, MNase-seq, ChIP-seq, RNA-seq, and raw DNA sequence as input to predict Transcription Start Site-seq. The combined model radically improves performance over separately trained datatype-specific networks, suggesting that it learns the synergistic relationship between datasets.</p>
-<p>Multi-task learning is an approach related to transfer learning. In a multi-task learning framework, a model learns a number of tasks simultaneously such that features are shared across them. DeepSEA <span class="citation">[<a href="#ref-2UI1BZuD">165</a>]</span> implemented a multi-task joint learning of diverse chromatin factors sharing predictive features from raw DNA sequence. This allowed, for example, a sequence feature that is effective in recognizing binding of a specific TF to be simultaneously used by another predictor for a physically interacting TF. Similarly, TFImpute <span class="citation">[<a href="#ref-Qbtqlmhf">152</a>]</span> learned information shared across transcription factors and cell lines to predict cell-specific TF binding for TF-cell line combinations. Yoon et al. <span class="citation">[<a href="#ref-yUgE09ve">75</a>]</span> demonstrated that predicting the primary cancer site from cancer pathology reports together with its laterality substantially improved the performance for the latter task, suggesting that multi-task learning can effectively leverage the commonality between two tasks using a shared representation. A number of studies from the drug development section employed multi-task learning to predict chemical bioactivity <span class="citation">[<a href="#ref-1Dzz0P0qr">296</a>,<a href="#ref-yAoN5gTU">299</a>]</span> and drug toxicity <span class="citation">[<a href="#ref-Y1D0SZrO">300</a>,<a href="#ref-1BARarxfz">418</a>]</span>. Kearnes et al. <span class="citation">[<a href="#ref-uP7SgBVd">293</a>]</span> systematically compared single-task and multi-task models for ADMET properties. They showed that multi-task learning generally improve performance and smaller datasets tend to benefit more than larger datasets.</p>
-<p>The multi-task learning approach is complementary to multimodal and transfer learning. All three techniques can be used together in the same model. For example, Zhang et al. <span class="citation">[<a href="#ref-HlDY7trA">411</a>]</span> combined deep model-based transfer and multi-task learning for cross-domain image annotation. One could imagine extending that approach to multimodal inputs as well. A common characteristic of these methods is better generalization of extracted features at various hierarchical levels of abstraction, which is attained by leveraging relationships between various inputs and task objectives.</p>
-<p>Despite demonstrated improvements, transfer learning approaches also pose a number of challenges. As mentioned above, there are no theoretically sound principles for pre-training and fine-tuning. Most best practice recommendations are heuristic and have to take into account additional hyper-parameters that depend on specific deep architectures, sizes of the pre-training and target datasets, and similarity of domains. However, similarity of datasets and domains in transfer learning and relatedness of tasks in multi-task learning is difficult to access. Most studies address these limitations by empirical evaluation of the model using established best practices or heuristics and cross-validation. Unfortunately, negative results are typically not reported. Rajkomar et al. <span class="citation">[<a href="#ref-x6HXFAS4">54</a>]</span> showed that a deep CNN trained on natural images can boost radiology image classification performance. However, due to differences in imaging domains, the target task required either re-training the initial model from scratch with special pre-processing or fine-tuning of the whole network on radiographs with heavy data augmentation to avoid overfitting. Exclusively fine-tuning top layers led to much lower validation accuracy (81.4 versus 99.5). Fine-tuning the aforementioned Basset model required no more than one training pass. Otherwise, the model overfit new data <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span>. DeepChem successfully improved results for low-data drug discovery with one-shot learning for related tasks. However, it demonstrated clear limitations to cross-task generalization across unrelated tasks in one-shot models, specifically nuclear receptor assays and patient adverse reactions <span class="citation">[<a href="#ref-P4ixsM8i">308</a>]</span>.</p>
+<p>Multi-task learning is an approach related to transfer learning. In a multi-task learning framework, a model learns a number of tasks simultaneously such that features are shared across them. DeepSEA <span class="citation">[<a href="#ref-2UI1BZuD">165</a>]</span> implemented multi-task joint learning of diverse chromatin factors from raw DNA sequence. This allowed a sequence feature that was effective in recognizing binding of a specific TF to be simultaneously used by another predictor for a physically interacting TF. Similarly, TFImpute <span class="citation">[<a href="#ref-Qbtqlmhf">152</a>]</span> learned information shared across transcription factors and cell lines to predict cell-specific TF binding for TF-cell line combinations. Yoon et al. <span class="citation">[<a href="#ref-yUgE09ve">75</a>]</span> demonstrated that predicting the primary cancer site from cancer pathology reports together with its laterality substantially improved the performance for the latter task, indicating that multi-task learning can effectively leverage the commonality between two tasks using a shared representation. Many studies employed multi-task learning to predict chemical bioactivity <span class="citation">[<a href="#ref-1Dzz0P0qr">296</a>,<a href="#ref-yAoN5gTU">299</a>]</span> and drug toxicity <span class="citation">[<a href="#ref-Y1D0SZrO">300</a>,<a href="#ref-1BARarxfz">418</a>]</span>. Kearnes et al. <span class="citation">[<a href="#ref-uP7SgBVd">293</a>]</span> systematically compared single-task and multi-task models for ADMET properties and found that multi-task learning generally improved performance. Smaller datasets tended to benefit more than larger datasets.</p>
+<p>Multi-task learning is complementary to multimodal and transfer learning. All three techniques can be used together in the same model. For example, Zhang et al. <span class="citation">[<a href="#ref-HlDY7trA">411</a>]</span> combined deep model-based transfer and multi-task learning for cross-domain image annotation. One could imagine extending that approach to multimodal inputs as well. A common characteristic of these methods is better generalization of extracted features at various hierarchical levels of abstraction, which is attained by leveraging relationships between various inputs and task objectives.</p>
+<p>Despite demonstrated improvements, transfer learning approaches pose challenges. There are no theoretically sound principles for pre-training and fine-tuning. Best practice recommendations are heuristic and must account for additional hyper-parameters that depend on specific deep architectures, sizes of the pre-training and target datasets, and similarity of domains. However, similarity of datasets and domains in transfer learning and relatedness of tasks in multi-task learning is difficult to access. Most studies address these limitations by empirical evaluation of the model. Unfortunately, negative results are typically not reported. Rajkomar et al. <span class="citation">[<a href="#ref-x6HXFAS4">54</a>]</span> showed that a deep CNN trained on natural images can boost radiology image classification performance. However, due to differences in imaging domains, the target task required either re-training the initial model from scratch with special pre-processing or fine-tuning of the whole network on radiographs with heavy data augmentation to avoid overfitting. Exclusively fine-tuning top layers led to much lower validation accuracy (81.4 versus 99.5). Fine-tuning the aforementioned Basset model with more than one pass resulted in overfitting <span class="citation">[<a href="#ref-2CbHXoFn">174</a>]</span>. DeepChem successfully improved results for low-data drug discovery with one-shot learning for related tasks. However, it clearly demonstrated the limitations of cross-task generalization across unrelated tasks in one-shot models, specifically nuclear receptor assays and patient adverse reactions <span class="citation">[<a href="#ref-P4ixsM8i">308</a>]</span>.</p>
 <p>In the medical domain, multimodal, multi-task and transfer learning strategies not only inherit most methodological issues from natural image, text, and audio domains, but also pose domain-specific challenges. There is a compelling need for the development of privacy-preserving transfer learning algorithms, such as Private Aggregation of Teacher Ensembles <span class="citation">[<a href="#ref-b8DJ1u6W">119</a>]</span>. We suggest that these types of models deserve deeper investigation to establish sound theoretical guarantees and determine limits for the transferability of features between various closely related and distant learning tasks.</p>
 <h2 id="conclusions">Conclusions</h2>
-<p>Deep learning-based methods now match or surpass the previous state of the art in a diverse array of tasks in patient and disease categorization, fundamental biological study, genomics, and treatment development. Returning to our central question: given this rapid progress, has deep learning transformed the study of human disease? Though the answer is highly dependent on the specific domain and problem being addressed, we conclude that deep learning has not <em>yet</em> realized its transformative potential or induced a strategic inflection point. Despite its dominance over competing machine learning approaches in many of the areas reviewed here and quantitative improvements in predictive performance, deep learning has not yet definitively &quot;solved&quot; those problems.</p>
-<p>As an analogy, consider recent progress in conversational speech recognition. Since 2009 there have been drastic performance improvements, with error rates dropping from more than 20% to less than 6% <span class="citation">[<a href="#ref-nyjAIan4">419</a>]</span> and finally approaching or exceeding human performance in the past year <span class="citation">[<a href="#ref-M2OLWojE">420</a>,<a href="#ref-wKioubsT">421</a>]</span>. The phenomenal improvements on benchmark datasets are undeniable, but greatly reducing the error rate on these benchmarks did not fundamentally transform the domain. Widespread adoption of conversational speech technologies will require not only improvements over baseline methods but truly solving the problem, in this case exceeding human-level performance, as well as convincing users to embrace the technology <span class="citation">[<a href="#ref-nyjAIan4">419</a>]</span>. We see parallels to the healthcare domain, where achieving the full potential of deep learning will require outstanding predictive performance as well as acceptance and adoption by biologists and clinicians.</p>
-<p>Some of the areas we have discussed are closer to surpassing this lofty bar than others, generally those that are more similar to the non-biomedical tasks that are now monopolized by deep learning. In medical imaging, diabetic retinopathy <span class="citation">[<a href="#ref-1mJW6umJ">46</a>]</span>, diabetic macular edema <span class="citation">[<a href="#ref-1mJW6umJ">46</a>]</span>, tuberculosis <span class="citation">[<a href="#ref-Qve94Jra">55</a>]</span>, and skin lesion <span class="citation">[<a href="#ref-XnYNYoYB">4</a>]</span> classifiers are highly accurate and comparable to clinician performance in the latter case.</p>
-<p>In other domains, perfect accuracy will not be required because deep learning will be used primarily to prioritize experiments and assist discovery. For example, in chemical screening for drug discovery, a deep learning system that successfully identifies dozens or hundreds of target-specific, active small molecules from a massive search space would have immense practical value even if its overall precision is modest. In medical imaging, deep learning can point an expert to the most challenging cases that require manual review <span class="citation">[<a href="#ref-Qve94Jra">55</a>]</span>, though the risk of false negatives must be addressed. In protein structure prediction, errors in individual residue-residue contacts can be tolerated when using the contacts jointly for 3D structure modeling. Improved contact map predictions <span class="citation">[<a href="#ref-BhfjKSY3">27</a>]</span> have led to notable improvements in fold and 3D structure prediction for some of the most challenging proteins, such as membrane proteins <span class="citation">[<a href="#ref-39RPiE10">198</a>]</span>.</p>
-<p>Conversely, the most challenging tasks may be those in which predictions are used directly for downstream modeling or decision-making, especially in the clinic. As an example, errors in sequence variant calling will be amplified if they are used directly for GWAS. In addition, the stochasticity and complexity of biological systems implies that for some problems, for instance, predicting gene regulation in disease, perfect accuracy will be unattainable.</p>
-<p>We are witnessing deep learning models achieving human-level performance across a number of biomedical domains, and yet do not believe that biologists and clinicians will be displaced anytime soon. While deep learning methods will soon be (or already are) better than scientists at specific tasks, they may not fully grasp the bigger picture. Machine learning algorithms, including deep neural networks, are also prone to mistakes that humans are much less likely to make, such as misclassification of adversarial examples <span class="citation">[<a href="#ref-1Fel6Bdb8">422</a>,<a href="#ref-UtcyntjF">423</a>]</span>, a reminder that these algorithms do not understand the semantics of the objects presented. Despite progress in addressing some of these limitations <span class="citation">[<a href="#ref-AsLAb71x">424</a>,<a href="#ref-18lZK7fxH">425</a>]</span>, we expect human experts will retain their critical roles in the laboratory and clinic for the foreseeable future. In some cases, cooperation between human experts and deep learning algorithms can achieve better performance than either individually <span class="citation">[<a href="#ref-mbEp6jNr">66</a>]</span>. Especially for sample and patient classification tasks, we expect deep learning methods to complement and assist biomedical researchers rather than compete with or even replace them.</p>
-<p>Even if deep learning in biology and healthcare is not yet transformative today, we are extremely optimistic about its future. Given how rapidly deep learning is evolving, its full potential in biomedicine has not been explored. We have highlighted numerous challenges beyond improving training and predictive accuracy, such as preserving patient privacy and interpreting models. Ongoing research has begun to address these problems and shown they are not insurmountable. Deep learning offers the flexibility to model data in its most natural form, for example, longer DNA sequences instead of k-mers for transcription factor binding prediction and molecular graphs instead of pre-computed bit vectors for drug discovery. These flexible input feature representations have spurred creative modeling approaches that would be infeasible with other machine learning techniques. Unsupervised methods are currently less-developed than their supervised counterparts, but they may have the most potential because of how expensive and time-consuming it is to label large amounts of biomedical data. When deep learning algorithms can summarize very large collections of input data into interpretable models that spur scientists to ask questions that they did not know how to ask, it will be clear that deep learning has transformed biology and medicine.</p>
+<p>Deep learning-based methods now match or surpass the previous state of the art in a diverse array of tasks in patient and disease categorization, fundamental biological study, genomics, and treatment development. Returning to our central question: given this rapid progress, has deep learning transformed the study of human disease? Though the answer is highly dependent on the specific domain and problem being addressed, we conclude that deep learning has not <em>yet</em> realized its transformative potential or induced a strategic inflection point. Despite its dominance over competing machine learning approaches in many of the areas reviewed here and quantitative improvements in predictive performance, deep learning has not yet definitively &quot;solved&quot; these problems.</p>
+<p>As an analogy, consider recent progress in conversational speech recognition. Since 2009 there have been drastic performance improvements with error rates dropping from more than 20% to less than 6% <span class="citation">[<a href="#ref-nyjAIan4">419</a>]</span> and finally approaching or exceeding human performance in the past year <span class="citation">[<a href="#ref-M2OLWojE">420</a>,<a href="#ref-wKioubsT">421</a>]</span>. The phenomenal improvements on benchmark datasets are undeniable, but greatly reducing the error rate on these benchmarks did not fundamentally transform the domain. Widespread adoption of conversational speech technologies will require solving the problem, i.e. methods that surpass human performance, and convincing users to adopt them <span class="citation">[<a href="#ref-nyjAIan4">419</a>]</span>. We see parallels in healthcare, where achieving the full potential of deep learning will require outstanding predictive performance as well as acceptance and adoption by biologists and clinicians.</p>
+<p>Some of the areas we have discussed are closer to surpassing this lofty bar than others, generally those that are more similar to the non-biomedical tasks that are now monopolized by deep learning. In medical imaging, diabetic retinopathy <span class="citation">[<a href="#ref-1mJW6umJ">46</a>]</span>, diabetic macular edema <span class="citation">[<a href="#ref-1mJW6umJ">46</a>]</span>, tuberculosis <span class="citation">[<a href="#ref-Qve94Jra">55</a>]</span>, and skin lesion <span class="citation">[<a href="#ref-XnYNYoYB">4</a>]</span> classifiers are highly accurate and comparable to clinician performance.</p>
+<p>In other domains, perfect accuracy will not be required because deep learning will primarily prioritize experiments and assist discovery. For example, in chemical screening for drug discovery, a deep learning system that successfully identifies dozens or hundreds of target-specific, active small molecules from a massive search space would have immense practical value even if its overall precision is modest. In medical imaging, deep learning can point an expert to the most challenging cases that require manual review <span class="citation">[<a href="#ref-Qve94Jra">55</a>]</span>, though the risk of false negatives must be addressed. In protein structure prediction, errors in individual residue-residue contacts can be tolerated when using the contacts jointly for 3D structure modeling. Improved contact map predictions <span class="citation">[<a href="#ref-BhfjKSY3">27</a>]</span> have led to notable improvements in fold and 3D structure prediction for some of the most challenging proteins, such as membrane proteins <span class="citation">[<a href="#ref-39RPiE10">198</a>]</span>.</p>
+<p>Conversely, the most challenging tasks may be those in which predictions are used directly for downstream modeling or decision-making, especially in the clinic. As an example, errors in sequence variant calling will be amplified if they are used directly for GWAS. In addition, the stochasticity and complexity of biological systems implies that for some problems, for instance predicting gene regulation in disease, perfect accuracy will be unattainable.</p>
+<p>We are witnessing deep learning models achieving human-level performance across a number of biomedical domains. However, machine learning algorithms, including deep neural networks, are also prone to mistakes that humans are much less likely to make, such as misclassification of adversarial examples <span class="citation">[<a href="#ref-1Fel6Bdb8">422</a>,<a href="#ref-UtcyntjF">423</a>]</span>, a reminder that these algorithms do not understand the semantics of the objects presented. It may be impossible to guarantee that a model is not susceptible to adversarial examples, but work in this area is continuing <span class="citation">[<a href="#ref-AsLAb71x">424</a>,<a href="#ref-18lZK7fxH">425</a>]</span>. Cooperation between human experts and deep learning algorithms addresses many of these challenges and can achieve better performance than either individually <span class="citation">[<a href="#ref-mbEp6jNr">66</a>]</span>. For sample and patient classification tasks, we expect deep learning methods to augment clinicians and biomedical researchers.</p>
+<p>We are extremely optimistic about the future of deep learning in biology and medicine. Given how rapidly deep learning is evolving, its full potential in biomedicine has not been explored. We have highlighted numerous challenges beyond improving training and predictive accuracy, such as preserving patient privacy and interpreting models. Ongoing research has begun to address these problems and shown that they are not insurmountable. Deep learning offers the flexibility to model data in its most natural form, for example, longer DNA sequences instead of k-mers for transcription factor binding prediction and molecular graphs instead of pre-computed bit vectors for drug discovery. These flexible input feature representations have spurred creative modeling approaches that would be infeasible with other machine learning techniques. Unsupervised methods are currently less-developed than their supervised counterparts, but they may have the most potential because of how expensive and time-consuming it is to label large amounts of biomedical data. When deep learning algorithms can summarize very large collections of input data into interpretable models that spur scientists to ask questions that they did not know how to ask, it will be clear that deep learning has transformed biology and medicine.</p>
 <h2 id="methods">Methods</h2>
 <h3 id="continuous-collaborative-manuscript-drafting">Continuous collaborative manuscript drafting</h3>
 <p>We recognized that deep learning in precision medicine is a rapidly developing area. Hence, diverse expertise was required to provide a forward-looking perspective. Accordingly, we collaboratively wrote this review in the open, enabling anyone with expertise to contribute. We wrote the manuscript in markdown and tracked changes using git. Contributions were handled through GitHub, with individuals submitting &quot;pull requests&quot; to suggest additions to the manuscript.</p>
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zUmN_dHg@;-xx!aCJ}AU*Jm<ye^5))uMTO@*tqqDo3Z6c;vI!+wzZ5-JtyX;mR1*E`
z)gnEWphCG-Kib7bn=22#cIIu66=?38<g1aep|XPG{|AA~6Vzodh-`73KF?r&y;WBH
zcaD!NkxH-cf8bd6@Y|fZI@>1~F`B3V6+V2em}78Ls;N~`Bv8M5swQ0B^5>rBFF9|=
tPTXgv{6XM*<>tU269n2{8C_QDm0GF=QFiYG$20@CoSL?{Rg)zcEdX+kb$S2*

delta 248
zcmZ3+yo`B*VttRj;^#c0%ZyJYJ|ro>oLuxQe$~qY*_@!20<O8+R!cE`5YX&h=1{Ty
zn&qYAQrlS0q#xt>pzzB2z@f>yx(lvbO=h~ZZ`0Je1(UvC*nC%|SlH#_N6nB)KqaO}
zlP>%!*^={JvG-t^O>Gfl&i<BZ`ln8~TlCHQ_#w)T<NpT%F5%;H_RLXtO?23k?@X4s
z{hZ??OQhnfs~<SFCq3HDzFg(cWKl*l6`)Fuce~U$FXgTIAn1RL;qn6&xWdA`1?&0}
sUcH^1$7rGSLBL?g{3Cro&;P4iW?>?DUu7d)ojb#+{hllKOx$Y%08X@c+5i9m