{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yan B"],"funding":["National Natural Science Foundation of China","Fujian Science and Technology Plan Industry-University-Research Cooperation Project","National Key Research and Development Program of China"],"pagination":["3112"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9147181"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(10)"],"pubmed_abstract":["In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better"],"journal":["Molecules (Basel, Switzerland)"],"pubmed_title":["An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning."],"pmcid":["PMC9147181"],"funding_grant_id":["2021H6015","61772023","2019QY1803"],"pubmed_authors":["He S","Wu L","Liu K","Bo X","Yan B","Han J","Ye X","Wang J"],"additional_accession":[]},"is_claimable":false,"name":"An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.","description":"In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-04-19T12:08:48.632Z","creation":"2025-04-19T12:08:48.632Z"},"accession":"S-EPMC9147181","cross_references":{"pubmed":["35630587"],"doi":["10.3390/molecules27103112"]}}