ArticleBMC medical informatics and decision making2021
Learning from low-rank multimodal representations for predicting disease-drug associations.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Comic: explainable drug repurposing via contrastive masking for interpretable connections.BMC bioinformatics · 2026Article
- Heterogeneous biological graph convolutional network for drug-target interaction prediction.PloS one · 2026Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Ensemble techniques for predictive modeling of leishmanial activity via molecular fingerprints.BMC medical informatics and decision making · 2025Article
- A drug repurposing method based on inhibition effect on gene regulatory network.Computational and structural biotechnology journal · 2023Article
- Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.BMC bioinformatics · 2022Article
- Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents.Computational and structural biotechnology journal · 2022Review
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8 authors.
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Abstract
backgroundDisease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associations, it would be useful to develop computational methods for predicting unobserved disease-drug associations. With the advent of various datasets describing diseases and drugs, it has become more feasible to build a model describing the potential correlation between disease and drugs.
resultsIn this work, we propose a new prediction method, called LMFDA, which works in several stages. First, it studies the drug chemical structure, disease MeSH descriptors, disease-related phenotypic terms, and drug-drug interactions. On this basis, similarity networks of different sources are constructed to enrich the representation of drugs and diseases. Based on the fused disease similarity network and drug similarity network, LMFDA calculated the association score of each pair of diseases and drugs in the database. This method achieves good performance on Fdataset and Cdataset, AUROCs were 91.6% and 92.1% respectively, higher than many of the existing computational models.
conclusionsThe novelty of LMFDA lies in the introduction of multimodal fusion using low-rank tensors to fuse multiple similar networks and combine matrix complement technology to predict potential association. We have demonstrated that LMFDA can display excellent network integration ability for accurate disease-drug association inferring and achieve substantial improvement over the advanced approach. Overall, experimental results on two real-world networks dataset demonstrate that LMFDA able to delivers an excellent detecting performance. Results also suggest that perfecting similar networks with as much domain knowledge as possible is a promising direction for drug repositioning.
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