ArticleBMC medical genomics2013
Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.
Article in BMC medical genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- p16EMBO reports · 2026Article
- Drug-disease networks and drug repurposing.PLoS computational biology · 2025Article
- MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.Briefings in bioinformatics · 2024Article
- A novel approach for predicting upstream regulators (PURE) that affect gene expression.Scientific reports · 2023Article
- Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases.International journal of molecular sciences · 2022Article
- MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning.Frontiers in genetics · 2021Article
- Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization.Frontiers in bioengineering and biotechnology · 2020Article
- Fusion of multiple heterogeneous networks for predicting circRNA-disease associations.Scientific reports · 2019Article
- Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.Frontiers in chemistry · 2019Article
- Computational drug repositioning using meta-path-based semantic network analysis.BMC systems biology · 2018Article
- Predicting drug-disease associations by using similarity constrained matrix factorization.BMC bioinformatics · 2018Article
- DR2DI: a powerful computational tool for predicting novel drug-disease associations.Journal of computer-aided molecular design · 2018Article
- Article
- A computational method for the identification of candidate drugs for non-small cell lung cancer.PloS one · 2017Article
- Chemically induced skin carcinogenesis: Updates in experimental models (Review).Oncology reports · 2016Review
- In silico methods for drug repurposing and pharmacology.Wiley interdisciplinary reviews. Systems biology and medicine · 2016Review
- Illuminating drug action by network integration of disease genes: a case study of myocardial infarction.Molecular bioSystems · 2016Article
- An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.PloS one · 2016Article
- COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Methods for biological data integration: perspectives and challenges.Journal of the Royal Society, Interface · 2015Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDuring the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.
methodsWe combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.
resultsWe apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.
conclusionsWe clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.
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