{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2"],"submitter":["Galletti C"],"pubmed_abstract":["Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a target-centric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functiona"],"journal":["Frontiers in bioinformatics"],"pagination":["906644"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9580901"],"repository":["biostudies-literature"],"pubmed_title":["Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning."],"pmcid":["PMC9580901"],"pubmed_authors":["Fernandez-Fuentes N","Aguirre-Plans J","Galletti C","Oliva B"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning.","description":"Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a target-centric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functiona","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-19T12:04:50.747Z","creation":"2025-04-19T12:04:50.747Z"},"accession":"S-EPMC9580901","cross_references":{"pubmed":["36304303"],"doi":["10.3389/fbinf.2022.906644"]}}