ArticleBriefings in bioinformatics2021
Coupled matrix-matrix and coupled tensor-matrix completion methods for predicting drug-target interactions.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH.The annals of applied statistics · 2026Article
- No-rank tensor decomposition via metric learning.Scientific reports · 2026Article
- Enhanced drug-disease association prediction through representation learning on similarity networks.Biology methods & protocols · 2026Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.Molecules (Basel, Switzerland) · 2025Article
- Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.Environmental science & technology · 2024Article
- MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction.BMC bioinformatics · 2023Article
- Prediction of Drug-Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding.Molecules (Basel, Switzerland) · 2022Article
- Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents.Computational and structural biotechnology journal · 2022Review
- Drug repositioning based on heterogeneous networks and variational graph autoencoders.Frontiers in pharmacology · 2022Article
- LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions.Frontiers in microbiology · 2022Article
- CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions.Frontiers in molecular biosciences · 2022Article
- An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.BioMed research international · 2021Article
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
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Authors and funding
6 authors.
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Abstract
Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug-target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner. Matrix factorization methods have been proven to be the most reliable group of methods. Here, we first propose a matrix factorization-based method termed 'Coupled Matrix-Matrix Completion' (CMMC). Next, in order to utilize more comprehensive information provided in different databases and incorporate multiple types of scores for drug-drug similarities and target-target relationship, we then extend CMMC to 'Coupled Tensor-Matrix Completion' (CTMC) by considering drug-drug and target-target similarity/interaction tensors. Results: Evaluation on two benchmark datasets, DrugBank and TTD, shows that CTMC outperforms the matrix-factorization-based methods: GRMF, $L_{2,1}$-GRMF, NRLMF and NRLMF$\beta $. Based on the evaluation, CMMC and CTMC outperform the above three methods in term of area under the curve, F1 score, sensitivity and specificity in a considerably shorter run time.
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