- Introduction
- Package requirement
- Installation
- Data preprocess
- Model training and prediction
- Citation
- Contact
In this study, we propose a novel deep learning framework named WTHMDA (Weighted Taxonomic Heterogeneous-network based Microbe-Disease Association), which leverages a weighted graph convolution network and microbial taxonomy common tree for microbe-disease association prediction.
- torch==1.9.0
- dgl==0.9.1
- ete3==3.1.3
- gensim==4.0.1
sh init.sh
Then all tools are located at ‘bin’ folder:
get_disease_feature.py // To generate disease feature utilizing weighted Deepwalk
get_microbe_feature.py // To generate microbe feature utilizing weightedDeepwalk
get_edges.py // To generate directed microbe links and four types of weighted microbe ontological similarities
WTHMDA_run.py // For model training and prediction
To generate disease feature utilizing weighted Deepwalk, one file are required as input including integrated disease functional similarities.
a. integrated disease functional similarities (required)
| 1 | 0.21 | 0.52 | 0.11 |
|---|---|---|---|
| 0.21 | 1 | 0 | 0.44 |
| 0.52 | 0 | 1 | 0.87 |
| 0.11 | 0.44 | 0.87 | 1 |
To generate microbe feature utilizing weighted Deepwalk, one file are required as input including integrated microbe functional similarities. a. integrated microbe functional similarities (required)
| 1 | 0.75 | 0.9 | 0.43 |
|---|---|---|---|
| 0.75 | 1 | 0.71 | 0.1 |
| 0.9 | 0.71 | 1 | 0.53 |
| 0.43 | 0.1 | 0.53 | 1 |
To generate microbe feature utilizing weighted Deepwalk, one file are required as input including integrated microbe functional similarities. a. integrated microbe functional similarities (required)
| 0 | Abiotrophia |
|---|---|
| 1 | Bacteroides |
| 2 | Caloramator |
| 3 | Dermacoccus |
You can specify IDFS, IMFS and microbial lists by "--dis", "--mis" and "--mlist" respectively. We set up an example dataset(example) in the "data" folder for a quick start:
cd data
python ../bin/get_disease_feature.py --dis example/disease_I_S.csv
python ../bin/get_microbe_feature.py --mis example/microbe_I_S.csv
python ../bin/get_edges.py --mlist example/microbe_list.xlsx
To train WTHMDA model and prediction, two files are required as input including train_edge and test_edge. a. train_edge (required)
| disease | microbe | label |
|---|---|---|
| 1 | 108 | 1 |
| 4 | 43 | 0 |
| 36 | 230 | 1 |
b. test_edge (required)
| disease | microbe | label |
|---|---|---|
| 3 | 165 | 1 |
| 15 | 62 | 1 |
| 14 | 42 | 0 |
You can assign train data and test data by '--train' and '--test', respectively. In addition, you can specify the output path of model '--o'. We set an example dataset in ‘data’ folder for quick start. To train a Meta-Spec model: cd data python ../bin/meta_spec_train.py --microbe train_microbe_data.csv --host train_hosts_data.csv --label train_labels.csv --o out
Then, you can predict the status of microbiomes using the model generated by the training procedure. We set an example dataset(example) in ‘data/’ folder for quick start.
cd data
python ../bin/train_test.py --train example/train_edge.csv --test example/test_edge.csv --o ../result/
For convenience, you can run the processes above by running the example.sh in folder 'data/'.
cd data
chmod a+x example.sh
./example.sh
Other settings can be seen in the config.py.
All problems please contact WTHMDA development team: Xiaoquan Su Email: suxq@qdu.edu.cn