ArticleBMC bioinformatics2020
NEDD: a network embedding based method for predicting drug-disease associations.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Identification and validation of an endoplasmic reticulum stress related model to predict prognosis and tumor microenvironment in neuroblastoma.BMC cancer · 2026Article
- Enhanced drug disease association prediction through multimodal data integration and meta path guided global local feature fusion.Scientific reports · 2026Article
- Redox-linked hormetic responses of Withania coagulans (Stocks) Dunal to Mn, Zn, and Fe supplementation in vitro.BMC plant biology · 2026Article
- A quantitative study of cytotoxic compounds using graph based descriptors and machine learning.Scientific reports · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Mechanism-aware and explainable artificial intelligence for drug release prediction in polymeric drug delivery systems.Frontiers in medicine · 2026Article
- Multi criterion decision making analysis of hematologic cancer drugs via topological indices and physicochemical properties.Scientific reports · 2025Article
- The covalent docking software landscape: features and applications in drug design.Briefings in bioinformatics · 2025Review
- MRDDA: a multi-relational graph neural network for drug-disease association prediction.Journal of translational medicine · 2025Article
- Global burden and forecast of infectious diseases attributable to drug use: evidence from GBD 2021.Frontiers in public health · 2025Article
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases.
resultsIn this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases.
conclusionsThe experiments on a gold standard dataset which contains 1933 validated drug-disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.