ArticleBMC bioinformatics2015
Predicting drug side effects by multi-label learning and ensemble learning.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
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Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy-Antagonism in Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Pooled it
- Explainable drug side effect prediction in central neural system via biologically informed graph neural network.Translational psychiatry · 2026Article
- DrugForm-TAS: Target-Agnostic Selectivity as Proteome-wide Binding Propensity Estimation.Computational and structural biotechnology journal · 2026Article
- DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov-Arnold networks.Briefings in bioinformatics · 2025Article
- MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.Scientific reports · 2025Article
- Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions.Toxics · 2025Review
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.Drug safety · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.Scientific reports · 2024Article
- Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency.BMC bioinformatics · 2024Article
- Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.Molecules (Basel, Switzerland) · 2024Article
- Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction.Molecules (Basel, Switzerland) · 2023Article
- Article
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- Multimodal representation learning for predicting molecule-disease relations.Bioinformatics (Oxford, England) · 2023Article
- Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.Frontiers in pharmacology · 2023Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting drug side effects is an important topic in the drug discovery. Although several machine learning methods have been proposed to predict side effects, there is still space for improvements. Firstly, the side effect prediction is a multi-label learning task, and we can adopt the multi-label learning techniques for it. Secondly, drug-related features are associated with side effects, and feature dimensions have specific biological meanings. Recognizing critical dimensions and reducing irrelevant dimensions may help to reveal the causes of side effects.
methodsIn this paper, we propose a novel method 'feature selection-based multi-label k-nearest neighbor method' (FS-MLKNN), which can simultaneously determine critical feature dimensions and construct high-accuracy multi-label prediction models.
resultsComputational experiments demonstrate that FS-MLKNN leads to good performances as well as explainable results. To achieve better performances, we further develop the ensemble learning model by integrating individual feature-based FS-MLKNN models. When compared with other state-of-the-art methods, the ensemble method produces better performances on benchmark datasets.
conclusionsIn conclusion, FS-MLKNN and the ensemble method are promising tools for the side effect prediction. The source code and datasets are available in the Additional file 1.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.