ArticleComputational and mathematical methods in medicine2015
Network-based inference methods for drug repositioning.
Article in Computational and mathematical methods in medicine, 2015. 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.
- DMAPLM: A multimodal pretrained framework for computational drug repositioning.PLoS computational biology · 2026Article
- A Graph Neural Network Framework with Equivariance and Heterogeneity for Drug Repositioning.Interdisciplinary sciences, computational life sciences · 2026Article
- MDG-DDI: multi-feature drug graph for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- MRDDA: a multi-relational graph neural network for drug-disease association prediction.Journal of translational medicine · 2025Article
- A subspace learning aided matrix factorization for drug repurposing.BioImpacts : BI · 2025Article
- The OREGANO knowledge graph for computational drug repurposing.Scientific data · 2023Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19.Frontiers in microbiology · 2022Article
- A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities.Frontiers in pharmacology · 2022Article
- Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2.Structural chemistry · 2022Review
- In silico drug repositioning based on the integration of chemical, genomic and pharmacological spaces.BMC bioinformatics · 2021Article
- A data-driven methodology towards evaluating the potential of drug repurposing hypotheses.Computational and structural biotechnology journal · 2021Article
- A Review of CurrentFrontiers in oncology · 2021Review
- Drug repurposing for breast cancer therapy: Old weapon for new battle.Seminars in cancer biology · 2021Review
- Article
- iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding.PLoS computational biology · 2020Article
- Transcriptome-Guided Drug Repositioning.Pharmaceutics · 2019Article
- Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network.BMC bioinformatics · 2019Article
- A new computational drug repurposing method using established disease-drug pair knowledge.Bioinformatics (Oxford, England) · 2019Article
- HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association PredictionFrontiers in pharmacology · 2019Article
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5 authors.
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Abstract
Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.
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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.