ArticleBMC bioinformatics2019
The computational prediction of drug-disease interactions using the dual-network L
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.Briefings in bioinformatics · 2024Article
- Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction.BMC bioinformatics · 2024Article
- AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion.Cells · 2023Article
- Review
- Drug-target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization.BMC bioinformatics · 2021Article
- DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization.BMC bioinformatics · 2021Article
- MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning.Frontiers in genetics · 2021Article
- DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation.PloS one · 2021Article
- Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations.Frontiers in genetics · 2021Article
- Graph regularized LBMC bioinformatics · 2020Article
- RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations.BMC bioinformatics · 2019Article
- NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations.BMC bioinformatics · 2019Article
- Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.Frontiers in chemistry · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundPredicting drug-disease interactions (DDIs) is time-consuming and expensive. Improving the accuracy of prediction results is necessary, and it is crucial to develop a novel computing technology to predict new DDIs. The existing methods mostly use the construction of heterogeneous networks to predict new DDIs. However, the number of known interacting drug-disease pairs is small, so there will be many errors in this heterogeneous network that will interfere with the final results.
resultsA novel method, known as the dual-network L
conclusionsCross validation is used to evaluate our method, and simulation experiments are used to predict new interactions using two different datasets. Finally, our prediction accuracy is better than other existing methods. This proves that our method is feasible and effective.
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