ArticleComputational and mathematical methods in medicine2022
Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation.The Journal of international medical research · 2026Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods.Life (Basel, Switzerland) · 2023Article
- Extracts ofLife (Basel, Switzerland) · 2023Article
- Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.Frontiers in genetics · 2023Article
- Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.Life (Basel, Switzerland) · 2022Article
- Identification of methylation signatures associated with CAR T cell in B-cell acute lymphoblastic leukemia and non-hodgkin's lymphoma.Frontiers in oncology · 2022Article
- Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores.Frontiers in molecular neuroscience · 2022Article
- Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.Frontiers in microbiology · 2022Article
- Functional and embedding feature analysis for pan-cancer classification.Frontiers in oncology · 2022Article
- Characterization of spleen and lymph node cell typesFrontiers in molecular neuroscience · 2022Article
- Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8Frontiers in genetics · 2022Article
- Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods.Frontiers in molecular biosciences · 2022Article
- Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer's Disease.Frontiers in neuroscience · 2022Article
- Identifying Functions of Proteins in Mice With Functional Embedding Features.Frontiers in genetics · 2022Article
- Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data.Frontiers in bioengineering and biotechnology · 2022Article
- Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods.Frontiers in bioengineering and biotechnology · 2022Article
- Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.Frontiers in molecular biosciences · 2022Article
- Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.BioMed research international · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drugs can treat different diseases but also bring side effects. Undetected and unaccepted side effects for approved drugs can greatly harm the human body and bring huge risks for pharmaceutical companies. Traditional experimental methods used to determine the side effects have several drawbacks, such as low efficiency and high cost. One alternative to achieve this purpose is to design computational methods. Previous studies modeled a binary classification problem by pairing drugs and side effects; however, their classifiers can only extract one feature from each type of drug association. The present work proposed a novel multiple-feature sampling scheme that can extract several features from one type of drug association. Thirteen classification algorithms were employed to construct classifiers with features yielded by such scheme. Their performance was greatly improved compared with that of the classifiers that use the features yielded by the original scheme. Best performance was observed for the classifier based on random forest with MCC of 0.8661, AUROC of 0.969, and AUPR of 0.977. Finally, one key parameter in the multiple-feature sampling scheme was analyzed.
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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 Socratic graph.