EGATMDA: Ensembling Graph Attention Networks for Human Microbe-Drug Association prediction.

• drugs: ID and names for drugs.
• microbes: ID and names for microbes.
• diseases: ID and names for diseases.
• adj: interaction pairs between drugs and microbes.
• drug_microbe_associations: associations between drugs and microbes.
• drug_disease_associations: associations between drugs and diseases.
• microbe_disease_associations: associations between microbesa and diseases.
• drug_drug_interaction: interactions between drugs.
• microbe_microbe_interaction: interactions between microbes.
• drug_features: pre-processing feature matrix for drugs.
• microbe_features: genome sequence feature matrix for microbes.
• interaction: known adjacent matrix for drugs and microbes.
• net1: known adjacent matrix for drugs and microbes, i.e., interaction.
• net2: virtual adjacent matrix for drugs and microbes obtained from network Net2.
• net3: virtual adjacent matrix for drugs and microbes obtained from network Net3.
• net123: integrated adjacent matrix for drugs and microbes by fusing net1, net1 and net3.

1. To generate training data and test data.
2. Run main.py to train the model and obtain the predicted scores for microbe-drug associations.

• EGATMDA is implemented to work under Python 3.7.
• Tensorflow
• numpy
• scipy
• sklearn