BioMapAI

The primary goal of BioMapAI is to connect high-dimensional biology data, $X$ to mixed-type output matrix, $Y$. Unlike traditional ML or DL classifiers that typically predict a single outcome, $y$, BioMapAI is designed to learn multiple objects, $Y=\left[y_1,\ y_2,\ \ldots,y_n\right]$, simultaneously within a single model. This approach allows for the simultaneous prediction of diverse clinical outcomes - including binary, categorical, continuous variables - with ‘omics profiles, thus address disease heterogeneity by tailoring each patient’s specific symptomatology.

To Train your BioMapAI

BioMapAI is a deep learning framework for multi-stage modeling of biological data. It first predicts intermediate targets (omics scores) and then maps them into a final outcome or classification label.

Features

• OmicScoreModel: Train a model to predict intermediate omic scores (Y).
• ScoreLayer: Build a simple layer (or sub-model) that converts omic scores (Y) into the final target (y0).
• ScoreYModel: Combine the trained omic score model with the ScoreLayer to get final predictions and metrics.
• WeightsAdjust (Optional): Fine-tune the relationship between Y and y0 for better performance.

Quick Start

1. Install Dependencies

   pip install numpy pandas tensorflow

2. Clone or Download This Repository

   This repository should include:

   • BioMapAI.py: Contains the classes and methods (OmicScoreModel, ScoreLayer, etc.).
   • example_data/: Folder containing train_data.csv and test_data.csv.
   • BioMapAI_Training_Tutorial.ipynb: Detailed notebook tutorial.

3. Run the Tutorial Notebook

   • Open OmicScoreModel_Tutorial.ipynb in Jupyter Notebook or JupyterLab.
   • Follow the cells step-by-step to:
     1. Load training and test data.
     2. Train the OmicScoreModel to predict intermediate scores (Y).
     3. Build a ScoreLayer to convert those scores into final predictions (y0).
     4. Evaluate the model performance on a test set.
     5. (Optional) Adjust weights to improve performance.

4. Customize or Extend

   • Tune hyperparameters (epochs, optimizer, batch size, etc.).
   • Add or remove features in the data CSV files.
   • Modify BioMapAI.py to create custom network architectures or loss functions.
   • Integrate advanced data preprocessing or feature engineering techniques.

Further Details

For an in-depth guide, check out the BioMapAI_Training_Tutorial.ipynb. It covers:

• Data loading and organization
• Model instantiation and training procedures
• How to evaluate intermediate and final predictions
• Strategies for adjusting the model to improve performance

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To Load and use our pretrained model for ME/CFS: DeepMECFS

We have used BioMapAI to build pretrained models specifically for ME/CFS omics data, called DeepMECFS.We trained BioMapAI on gut microbiome data (species abundance and KEGG gene abundance), plasma metabolome, high-throughput immune flow cytometry data, Quest lab measurements, and a combined omics file containing key features from all datasets. These models are located in the folder pretrained_model_DeepMECFS/ and can be applied directly to new ME/CFS datasets. Here we use one of public metabolome datasets as an example to walk through how to load and use our pretrained models.

DeepMECFS Contents

• DeepMECFS_metabolome/: Directory containing the trained TensorFlow model.
• Y2y_metabolome/: Secondary model for converting intermediate features (Y) into final ME/CFS classification.
• metabolome_feature_metadata.csv: Required features and metadata for alignment with your dataset.

How to Use the Pretrained Model

1. Install/Clone the repository containing the pretrained_model_DeepMECFS/ folder.
2. Prepare Your Data:
   • Ensure your metabolomics data columns match the names (or COMP_IDs) in metabolome_feature_metadata.csv.
   • Scale or normalize your data consistently (e.g., via StandardScaler).
3. Run the Tutorial:
   • Open DeepMECFS_Tutorial.ipynb (or equivalent notebook/script).
   • Follow each step to:
     1. Load the pretrained models (DeepMECFS_metabolome/ and Y2y_metabolome/).
     2. Align your dataset columns to the model’s expected features.
     3. Generate predictions (ME/CFS vs. Control).
     4. Evaluate performance metrics (accuracy, AUC, precision, etc.).
4. Interpret Results:
   • The model outputs a probability (0 to 1) for ME/CFS classification.
   • You can threshold this probability (e.g., 0.5) to get a binary label (CFS vs. Control).
5. Explore Further:
   • You can experiment with different preprocessing or consider re-training parts of the pipeline if your data differs significantly from the original study.

Reference

The metabolomics data used to train DeepMECFS is described in:

Arnaud Germain, et al. “Plasma metabolomics reveals disrupted response and recovery following maximal exercise in myalgic encephalomyelitis/chronic fatigue syndrome.” JCI Insight. 2022;7(9):e157621.
DOI: 10.1172/jci.insight.157621

For detailed instructions, see the Pretrained_DeepMECFS_Tutorial.ipynb. It includes code snippets for loading the data, aligning it to the model’s features, and running inference.

License

This project is provided under the MIT License (or whichever license you choose). Feel free to modify or redistribute under its terms.

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