REDDA

Code and Dataset for "REDDA: integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction".

Reference

If you make advantage of the REDDA model or use the datasets released in our paper, please cite the following in your manuscript:

@article{10.1016/j.compbiomed.2022.106127,
author = {Yaowen Gu, Si Zheng, Qijin Yin, Rui Jiang, Jiao Li},
title = "{REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction}",
journal = {Computers in Biology and Medicine},
year = {2022},
month = {11},
issn = {0010-4825},
doi = {10.1016/j.compbiomed.2022.106127},
}

Benchmark Dataset

Our proposed drug repositioning benchmark dataset (Kdataset), including 894 drugs, 454 diseases, 18,877 proteins, 20,561 genes, 314 pathways, 2,704 drug-disease associations, 4,397 drug-protein associations, 18,545 protein-gene associations, 25,995 gene-pathway associations, 19,530 pathway-disease associations, 201,382 protein-protein interactions, 712,546 gene-gene interactions, and 1,669 pathway-pathway interactions. The files are as shown:

Omics
The node mappings of benchmark identifiers and external identifiers.

• drug.csv
  Benchmark IDs -- DrugBank IDs -- SMILES strings
• protein.csv
  Benchmark IDs -- UniProt IDs -- Amino acid sequences
• gene.csv
  Benchmark IDs -- Entrez IDs
• pathway.csv
  Benchmark IDs -- KEGG IDs
• disease.csv
  Benchmark IDs -- MeSH IDs

Interactions
The edges whose start nodes and destination nodes belong to the same node type.

• drug-drug.csv
  Drug1 IDs -- Drug2 IDs -- ECFP4 similarity
• protein-protein.csv
  Protein1 IDs -- Protein2 IDs -- Combined score (extracted from STRING)
• gene-gene.csv
  Gene1 IDs -- Gene2 IDs
• pathway-pathway.csv
  Pathway1 IDs -- Pathway2 IDs
• disease-disease.csv
  Disease1 IDs -- Disease2 IDs -- MeSH similarity

Associations
The edges whose start nodes and destination nodes belong to different node types.

• drug-protein.csv
  Drug IDs -- Protein IDs
• protein-gene.csv
  Protein IDs -- Gene IDs
• gene-pathway.csv
  Gene IDs -- Pathway IDs
• pathway-disease.csv
  Pathway IDs -- Disease IDs
• Kdataset.csv
  Drug IDs -- Disease IDs

Other files:

• drug_drug_baseline.csv: binarized drug-drug matrix with a demension of 894×894. Note that the binary values are calculated by a Top15 filtering of drug-drug similarity.

• disease_disease_baseline.csv: binarized disease-disease matrix with a demension of 454×454. Note that the binary values are calculated by a Top15 filtering of disease-disease similarity.

• Kdataset_baseline.csv: binarized drug-disease matirx with a demension of 894×454.

Similarly, a re-curated B-dataset is also used and stored in this repo, with the same file naming and hierarchy division, including 269 drugs, 598 diseases, 6,040 proteins, 18,416 drug-disease associations, 2,107 drug-protein associations, 17,631 protein-disease associations, and 592,926 protein-protein interactions.

REDDA model

Requirement

Pytorch >= 1.7.0

DGL >= 0.5.2

Run

python main.py -id {DEVICE ID} -da Kdataset_baseline -sp {SAVED PATH}
Optional Argument:
  -fo Number of k-folds cross-validation
  -ep Number of epoches
  -lr Learning rate
  -wd Weight decay
  -pa Patience in early stopping
  -hf Dimension of hiddent feats
  -he Number of heads in graph attention
  -dp Dropout rate

Contact

We welcome you to contact us (email: gu.yaowen@imicams.ac.cn) for any questions and cooperations.