用于对克隆造血中的驱动突变进行分类的基因特异性机器学习模型。
Gene-Specific Machine Learning Models to Classify Driver Mutations in Clonal Hematopoiesis.
发表日期：2024 Sep 04
作者：
Christopher M Arends, Siddhartha Jaiswal
来源：
Cancer Discovery
摘要：
对于能够驱动造血干细胞中与年龄相关的克隆扩增（称为克隆造血作用）的一组突变，尚未达成普遍共识，并且当前的变异分类通常依赖于专家知识得出的规则。在本期《Cancer Discovery》中，Damajo 及其同事在不事先了解克隆造血驱动突变的情况下训练和验证了机器学习模型，以纯粹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。参见 Demajo 等人的相关文章，第 17 页。 1717 (9).©2024 美国癌症研究协会。
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).©2024 American Association for Cancer Research.