{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wissel D"],"funding":["Swiss Government Excellence Scholarship","Swiss National Science Foundation","SDSC","European Union’s Horizon 2020","European Union's Horizon 2020"],"pagination":["bbaf521"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12486238"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(5)"],"pubmed_abstract":["Multi-omics data, which include genomic, transcriptomic, epigenetic, and proteomic data, are gaining increasing importance for determining the clinical outcomes of cancer patients. Several recent studies have evaluated various multimodal integration strategies for cancer survival prediction, highlighting the need for standardizing model performance results. Addressing this issue, we introduce SurvBoard, a benchmark framework that standardizes key experimental design choices. SurvBoard enables comparisons between single-cancer and pan-cancer data models and assesses the benefits of using patient data with missing modalities. We also address common pitfalls in preprocessing and validating multi-omics cancer survival models. We apply SurvBoard to several exemplary use cases, further confirmin"],"journal":["Briefings in bioinformatics"],"pubmed_title":["SurvBoard: standardized benchmarking for multi-omics cancer survival models."],"pmcid":["PMC12486238"],"funding_grant_id":["CRSII5 193832","826121","2021.0468"],"pubmed_authors":["Janakarajan N","Grover A","Boeva V","Toniato E","Wissel D","Martinez MR"],"additional_accession":[]},"is_claimable":false,"name":"SurvBoard: standardized benchmarking for multi-omics cancer survival models.","description":"Multi-omics data, which include genomic, transcriptomic, epigenetic, and proteomic data, are gaining increasing importance for determining the clinical outcomes of cancer patients. Several recent studies have evaluated various multimodal integration strategies for cancer survival prediction, highlighting the need for standardizing model performance results. Addressing this issue, we introduce SurvBoard, a benchmark framework that standardizes key experimental design choices. SurvBoard enables comparisons between single-cancer and pan-cancer data models and assesses the benefits of using patient data with missing modalities. We also address common pitfalls in preprocessing and validating multi-omics cancer survival models. We apply SurvBoard to several exemplary use cases, further confirmin","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-06-04T01:42:14.486Z","creation":"2026-05-04T03:13:12.333Z"},"accession":"S-EPMC12486238","cross_references":{"pubmed":["41031875"],"doi":["10.1093/bib/bbaf521"]}}