WTHMDA

Contents

• Introduction
• Package requirement
• Installation
• Data preprocess
• Model training and prediction
• Citation
• Contact

Introduction

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.

Package requirement

• torch==1.9.0
• dgl==0.9.1
• ete3==3.1.3
• gensim==4.0.1

Installation

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

Data preprocess

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

Model training and prediction

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.

Citation

Contact

All problems please contact WTHMDA development team: Xiaoquan Su    Email: suxq@qdu.edu.cn