{"title":"Simplifying clinical use of TCGA molecular subtypes through machine learning models","authors":"Kevin M. Boehm, Francisco Sánchez-Vega","doi":"10.1016/j.ccell.2024.12.009","DOIUrl":null,"url":null,"abstract":"In this issue of <em>Cancer Cell,</em> Ellrott et al. present machine learning models to classify samples into The Cancer Genome Atlas molecular subtypes using compact sets of genomic features. These validated, ready-to-use models are publicly available, although some clinical hurdles need to be cleared before they are fully implemented.","PeriodicalId":9670,"journal":{"name":"Cancer Cell","volume":"28 1","pages":""},"PeriodicalIF":48.8000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Cell","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.ccell.2024.12.009","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
In this issue of Cancer Cell, Ellrott et al. present machine learning models to classify samples into The Cancer Genome Atlas molecular subtypes using compact sets of genomic features. These validated, ready-to-use models are publicly available, although some clinical hurdles need to be cleared before they are fully implemented.
期刊介绍：
Cancer Cell is a journal that focuses on promoting major advances in cancer research and oncology. The primary criteria for considering manuscripts are as follows:
Major advances: Manuscripts should provide significant advancements in answering important questions related to naturally occurring cancers.
Translational research: The journal welcomes translational research, which involves the application of basic scientific findings to human health and clinical practice.
Clinical investigations: Cancer Cell is interested in publishing clinical investigations that contribute to establishing new paradigms in the treatment, diagnosis, or prevention of cancers.
Insights into cancer biology: The journal values clinical investigations that provide important insights into cancer biology beyond what has been revealed by preclinical studies.
Mechanism-based proof-of-principle studies: Cancer Cell encourages the publication of mechanism-based proof-of-principle clinical studies, which demonstrate the feasibility of a specific therapeutic approach or diagnostic test.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
