biostudies-literatureWen GNational Natural Science Foundation of Chinabtae684https://www.ebi.ac.uk/biostudies/studies/S-EPMC11673192biostudies-literatureUnknown41(1)<h4>Motivation</h4>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.<h4>Results</h4>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 CBioinformatics (Oxford, England)MMOSurv: meta-learning for few-shot survival analysis with multi-omics data.PMC1167319212222115Li LWen GfalseMMOSurv: meta-learning for few-shot survival analysis with multi-omics data.<h4>Motivation</h4>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.<h4>Results</h4>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 C2024-01-01T00:00:00Z2024 Dec2025-04-04T02:14:26.802Z2025-04-04T02:14:26.802ZS-EPMC116731923956348210.1093/bioinformatics/btae684