weiba / HGCNMDA

Predicting miRNA-disease associations from miRNA-gene-disease heterogeneous network with multi-relational graph convolutional network model

HGCNMDA

This work develops a multi-relational graph convolutional network model, namely HGCNMDA, to perform a MiRNA-Disease Association prediction task. 

Example

To run HGCNMDA on your data, execute the following command from the project home directory:
'python main.py'.

Dependencies

HGCNMDA was implemented with python 3.6.3. To run HGCNMDA, you need these packages:    
torch (1.5.0)            
torch-scatter (2.0.5)
torch-sparse (0.6.7)
torch-geometric (1.6.1)
numpy (1.18.5)
pandas (0.20.3)
networkx (2.0)
scikit-learn (0.19.1)

Input

the input files include:
disease-gene associations, miRNA-gene associations, miRNA-disease associations, disease similarity data, miRNA similarity data, and gene network.

output

The AUC of the test data based on HGCNMDA.