{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wen G"],"funding":["National Natural Science Foundation of China"],"pagination":["btae684"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11673192"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(1)"],"pubmed_abstract":["Motivation
High-throughput techniques have produced a large amount of high-dimensional multi-omics data, which makes it promising to predict patient survival outcomes more accurately. Recent work has showed the superiority of multi-omics data in survival analysis. However, it remains challenging to integrate multi-omics data to solve few-shot survival prediction problem, with only a few available training samples, especially for rare cancers.Results
In this work, we propose a meta-learning framework for multi-omics few-shot survival analysis, namely MMOSurv, which enables to learn an effective multi-omics survival prediction model from a very few training samples of a specific cancer type, with the meta-knowledge across tasks from relevant cancer types. By assuming a deep C"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["MMOSurv: meta-learning for few-shot survival analysis with multi-omics data."],"pmcid":["PMC11673192"],"funding_grant_id":["12222115"],"pubmed_authors":["Li L","Wen G"],"additional_accession":[]},"is_claimable":false,"name":"MMOSurv: meta-learning for few-shot survival analysis with multi-omics data.","description":"Motivation
High-throughput techniques have produced a large amount of high-dimensional multi-omics data, which makes it promising to predict patient survival outcomes more accurately. Recent work has showed the superiority of multi-omics data in survival analysis. However, it remains challenging to integrate multi-omics data to solve few-shot survival prediction problem, with only a few available training samples, especially for rare cancers.Results
In this work, we propose a meta-learning framework for multi-omics few-shot survival analysis, namely MMOSurv, which enables to learn an effective multi-omics survival prediction model from a very few training samples of a specific cancer type, with the meta-knowledge across tasks from relevant cancer types. By assuming a deep C","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2025-04-04T02:14:26.802Z","creation":"2025-04-04T02:14:26.802Z"},"accession":"S-EPMC11673192","cross_references":{"pubmed":["39563482"],"doi":["10.1093/bioinformatics/btae684"]}}