通过机器学习模型简化TCGA分子亚型的临床应用

IF 48.8 1区医学 Q1 CELL BIOLOGY

Cancer Cell Pub Date : 2025-01-16 DOI:10.1016/j.ccell.2024.12.009

Kevin M. Boehm, Francisco Sánchez-Vega

引用次数: 0

摘要

在这一期的《癌症细胞》中，Ellrott等人提出了机器学习模型，利用紧凑的基因组特征集将样本分类为The Cancer Genome Atlas分子亚型。这些经过验证的可随时使用的模型是公开的，尽管在完全实施之前需要清除一些临床障碍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Simplifying clinical use of TCGA molecular subtypes through machine learning models

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.

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来源期刊

Cancer Cell 医学-肿瘤学

CiteScore

55.20

自引率

1.20%

发文量

179

审稿时长

4-8 weeks

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.