{"title":"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","authors":"Christopher M Arends, Siddhartha Jaiswal","doi":"10.1158/2159-8290.CD-24-0751","DOIUrl":null,"url":null,"abstract":"<p><p>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).</p>","PeriodicalId":9430,"journal":{"name":"Cancer discovery","volume":null,"pages":null},"PeriodicalIF":29.7000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gene-Specific Machine Learning Models to Classify Driver Mutations in Clonal Hematopoiesis.\",\"authors\":\"Christopher M Arends, Siddhartha Jaiswal\",\"doi\":\"10.1158/2159-8290.CD-24-0751\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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).</p>\",\"PeriodicalId\":9430,\"journal\":{\"name\":\"Cancer discovery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":29.7000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer discovery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/2159-8290.CD-24-0751\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer discovery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/2159-8290.CD-24-0751","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
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).
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.