ArticlePLoS computational biology2019
Overlap matrix completion for predicting drug-associated indications.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 49 citations in OpenAlex.
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- DMAPLM: A multimodal pretrained framework for computational drug repositioning.PLoS computational biology · 2026Article
- From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing.bioRxiv : the preprint server for biology · 2026Article
- FKSUDDAPre: A drug-disease association prediction framework based on F-TEST feature selection and AMDKSU resampling with interpretability analysis.PLoS computational biology · 2026Article
- Benchmarking heterogeneous network-based methods for drug repurposing.NPJ systems biology and applications · 2025Article
- A Novel Drug-Disease Association Prediction Method Based on Deep Non-Negative Matrix Factorization with Local Graph Feature.Interdisciplinary sciences, computational life sciences · 2025Article
- Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.International journal of molecular sciences · 2024Article
- A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.Briefings in bioinformatics · 2024Article
- Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning.Biomedicines · 2023Article
- BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.Interdisciplinary sciences, computational life sciences · 2023Article
- Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.Biomolecules · 2022Review
- MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network.BMC bioinformatics · 2022Article
- Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.Journal of cellular and molecular medicine · 2022Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- Double matrix completion for circRNA-disease association prediction.BMC bioinformatics · 2021Article
- DDA-SKF: Predicting Drug-Disease Associations Using Similarity Kernel Fusion.Frontiers in pharmacology · 2021Review
- PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences.BMC bioinformatics · 2020Article
- Drug targets for COVID-19 therapeutics: Ongoing global efforts.Journal of biosciences · 2020Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications.
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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.