ArticlePloS one2017
SELF-BLM: Prediction of drug-target interactions via self-training SVM.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- ProDTI: prior-guided positive-unlabeled learning for robust drug-target interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Identifying novel therapeutic targets of natural compounds in traditional Chinese medicine herbs with hypergraph representation learning.Briefings in bioinformatics · 2025Article
- BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.Journal of chemical theory and computation · 2025Article
- Next-generation non-animal models for inflammatory bowel disease:NAM journal · 2025Review
- Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization.PloS one · 2025Article
- An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.Current computer-aided drug design · 2025Article
- Hierarchical multimodal self-attention-based graph neural network for DTI prediction.Briefings in bioinformatics · 2024Article
- Combination therapy synergism prediction for virus treatment using machine learning models.PloS one · 2024Article
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.BMC bioinformatics · 2023Article
- SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.BMC bioinformatics · 2023Article
- Modern drug discovery for inflammatory bowel disease: The role of computational methods.World journal of gastroenterology · 2023Review
- Validation approaches for computational drug repurposing: a review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Review
- An ensemble-based drug-target interaction prediction approach using multiple feature information with data balancing.Journal of biological engineering · 2022Article
- Drug-protein interaction predictionFrontiers in genetics · 2022Article
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.Journal of computer-aided molecular design · 2021Article
- Drug-target affinity prediction using graph neural network and contact maps.RSC advances · 2020Article
Corrections and comments
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2 authors.
Funding
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Abstract
Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.
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