{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huang Y"],"funding":["Guangdong Basic and Applied Basic Research Foundation"],"pagination":["742"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12292072"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(7)"],"pubmed_abstract":["The TRIM CIV subfamily, distinguished by its C-terminal PRY-SPRY domains, constitutes nearly half of the human TRIM family and plays pivotal roles in cancer progression through ubiquitination. Identifying TRIM CIV substrates and interactors has emerged as a critical approach for elucidating tumorigenesis. Current protein-protein interaction (PPI) prediction models face challenges, including an inherent deficiency of negative datasets, biased feature integration, and the absence of a cancer-specific interaction context. To achieve the precise identification of TRIMCIV targets, we developed TRIMCIVtargeter with predictive models that systematically integrates multi-dimensional PPI features-expression differences and correlations in specific cancer, comparable protein-docking scores, and canc"],"journal":["Biology"],"pubmed_title":["A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration."],"pmcid":["PMC12292072"],"funding_grant_id":["2023A1515011221"],"pubmed_authors":["Liu X","Huang Y","Liang J","Luo Y","Gao X","Xuan J","Liu W"],"additional_accession":[]},"is_claimable":false,"name":"A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration.","description":"The TRIM CIV subfamily, distinguished by its C-terminal PRY-SPRY domains, constitutes nearly half of the human TRIM family and plays pivotal roles in cancer progression through ubiquitination. Identifying TRIM CIV substrates and interactors has emerged as a critical approach for elucidating tumorigenesis. Current protein-protein interaction (PPI) prediction models face challenges, including an inherent deficiency of negative datasets, biased feature integration, and the absence of a cancer-specific interaction context. To achieve the precise identification of TRIMCIV targets, we developed TRIMCIVtargeter with predictive models that systematically integrates multi-dimensional PPI features-expression differences and correlations in specific cancer, comparable protein-docking scores, and canc","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jun","modification":"2026-07-04T03:17:53.493Z","creation":"2025-08-28T03:09:19.858Z"},"accession":"S-EPMC12292072","cross_references":{"pubmed":["40723303"],"doi":["10.3390/biology14070742"]}}