{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:31:05Z","timestamp":1773217865039,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12222115"],"award-info":[{"award-number":["12222115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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 Cox survival model with multiple omics, MMOSurv first learns an adaptable parameter initialization for the multi-omics survival model from abundant data of relevant cancers, and then adapts the parameters quickly and efficiently for the target cancer task with a very few training samples. Our experiments on eleven cancer types in The Cancer Genome Atlas datasets show that, compared to single-omics meta-learning methods, MMOSurv can better utilize the meta-information of similarities and relationships between different omics data from relevant cancer datasets to improve survival prediction of the target cancer with a very few multi-omics training samples. Furthermore, MMOSurv achieves better prediction performance than other state-of-the-art strategies such as multitask learning and pretraining.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>MMOSurv is freely available at https:\/\/github.com\/LiminLi-xjtu\/MMOSurv<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae684","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T08:37:04Z","timestamp":1732091824000},"source":"Crossref","is-referenced-by-count":5,"title":["MMOSurv: meta-learning for few-shot survival analysis with multi-omics data"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0535-1041","authenticated-orcid":false,"given":"Gang","family":"Wen","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710049,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3572-6832","authenticated-orcid":false,"given":"Limin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710049,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"2024122714113256400_btae684-B1","first-page":"205","author":"Amit","year":"2018"},{"key":"2024122714113256400_btae684-B2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/nm.4439","article-title":"The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions","volume":"24","author":"Bolouri","year":"2018","journal-title":"Nat Med"},{"key":"2024122714113256400_btae684-B3","doi-asserted-by":"crossref","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","article-title":"Deep 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