ArticleComputational and mathematical methods in medicine2015
Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space.
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 21 papers.
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Who cites it
21 citing papers in PubMed.
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare.BMC medical informatics and decision making · 2026Article
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- Dual-route embedding-aware graph neural networks for drug repositioning.Briefings in bioinformatics · 2025Article
- Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Drug repositioning based on residual attention network and free multiscale adversarial training.BMC bioinformatics · 2024Article
- A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data.Current opinion in structural biology · 2024Review
- Article
- KG-Predict: A knowledge graph computational framework for drug repurposing.Journal of biomedical informatics · 2022Article
- Article
- Drug repositioning based on heterogeneous networks and variational graph autoencoders.Frontiers in pharmacology · 2022Article
- HeTDR: Drug repositioning based on heterogeneous networks and text mining.Patterns (New York, N.Y.) · 2021Article
- SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer.Frontiers in neurorobotics · 2021Article
- A Review of CurrentFrontiers in oncology · 2021Review
- Graph embedding on biomedical networks: methods, applications and evaluations.Bioinformatics (Oxford, England) · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Overlap matrix completion for predicting drug-associated indications.PLoS computational biology · 2019Article
- Additional Neural Matrix Factorization model for computational drug repositioning.BMC bioinformatics · 2019Article
- Drug repositioning based on bounded nuclear norm regularization.Bioinformatics (Oxford, England) · 2019Article
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space, which includes drug-gene interactions, disease-gene interactions, and gene-gene interactions, is aimed at providing molecular biological information for prediction of drug-disease associations. The rationality lies in our belief that association between drug and disease has its evidence in the interactome network of genes. Experiments show that the integration of genomic space is indeed effective. Drugs, diseases, and genes are described with feature vectors of the same dimension, which are retrieved from the interaction data. Then a matrix factorization model is set up to quantify the association between drugs and diseases. Finally, we use the matrix factorization model to predict novel indications for drugs.
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