利用基因特异性机器学习模型对克隆性造血中的驱动突变进行分类

IF 29.7 1区 医学 Q1 ONCOLOGY
Christopher M Arends, Siddhartha Jaiswal
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引用次数: 0

摘要

对于能够驱动被称为克隆性造血的造血干细胞中与年龄相关的克隆扩增的突变集,目前还没有达成普遍共识,目前的变异分类通常依赖于从专家知识中得出的规则。在本期的《癌症发现》(Cancer Discovery)杂志上,Damajo及其同事训练并验证了机器学习模型,无需事先了解克隆性造血驱动突变的知识,就能以纯数据驱动的方式对血液中12个基因的体细胞突变进行分类。请参见 Demajo 等人的相关文章,第 1717 页(9)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gene-Specific Machine Learning Models to Classify Driver Mutations in Clonal Hematopoiesis.

There is no general consensus on the set of mutations capable of driving the age-related clonal expansions in hematopoietic stem cells known as clonal hematopoiesis, and current variant classifications typically rely on rules derived from expert knowledge. In this issue of Cancer Discovery, Damajo and colleagues trained and validated machine learning models without prior knowledge of clonal hematopoiesis driver mutations to classify somatic mutations in blood for 12 genes in a purely data-driven way. See related article by Demajo et al., p. 1717 (9).

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来源期刊
Cancer discovery
Cancer discovery ONCOLOGY-
CiteScore
22.90
自引率
1.40%
发文量
838
审稿时长
6-12 weeks
期刊介绍: Cancer Discovery publishes high-impact, peer-reviewed articles detailing significant advances in both research and clinical trials. Serving as a premier cancer information resource, the journal also features Review Articles, Perspectives, Commentaries, News stories, and Research Watch summaries to keep readers abreast of the latest findings in the field. Covering a wide range of topics, from laboratory research to clinical trials and epidemiologic studies, Cancer Discovery spans the entire spectrum of cancer research and medicine.
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