ArticleBMC systems biology2016
The extraction of drug-disease correlations based on module distance in incomplete human interactome.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- MGACL: Prediction Drug-Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning.Biomolecules · 2024Article
- A drug repurposing method based on inhibition effect on gene regulatory network.Computational and structural biotechnology journal · 2023Article
- A Visual Phenotype-Based Differential Diagnosis Process for Rare Diseases.Interdisciplinary sciences, computational life sciences · 2022Article
- RDmap: a map for exploring rare diseases.Orphanet journal of rare diseases · 2021Article
- Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways.PLoS computational biology · 2021Article
- Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model.International journal of molecular sciences · 2020Article
- Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression.Frontiers in bioengineering and biotechnology · 2020Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- Application of Machine Learning in Microbiology.Frontiers in microbiology · 2019Review
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- Computational drug repositioning using meta-path-based semantic network analysis.BMC systems biology · 2018Article
- Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity.International journal of biological sciences · 2018Article
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4 authors.
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Abstract
backgroundExtracting drug-disease correlations is crucial in unveiling disease mechanisms, as well as discovering new indications of available drugs, or drug repositioning. Both the interactome and the knowledge of disease-associated and drug-associated genes remain incomplete.
resultsWe present a new method to predict the associations between drugs and diseases. Our method is based on a module distance, which is originally proposed to calculate distances between modules in incomplete human interactome. We first map all the disease genes and drug genes to a combined protein interaction network. Then based on the module distance, we calculate the distances between drug gene sets and disease gene sets, and take the distances as the relationships of drug-disease pairs. We also filter possible false positive drug-disease correlations by p-value. Finally, we validate the top-100 drug-disease associations related to six drugs in the predicted results.
conclusionThe overlapping between our predicted correlations with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrate our approach can not only effectively identify new drug indications, but also provide new insight into drug-disease discovery.
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