{"title":"用于癌症类型预测的深度学习模型树立了新标准。","authors":"Salil Garg","doi":"10.1158/2159-8290.CD-24-0280","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary. In this issue of Cancer Discovery, Darmofal and colleagues describe a new tool that uses information from a clinical sequencing panel to diagnose tumor type, and show that the model is particularly robust. See related article by Darmofal et al., p. 1064 (1).</p>","PeriodicalId":9430,"journal":{"name":"Cancer discovery","volume":null,"pages":null},"PeriodicalIF":29.7000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Deep Learning Model for Cancer Type Prediction Sets a New Standard.\",\"authors\":\"Salil Garg\",\"doi\":\"10.1158/2159-8290.CD-24-0280\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Summary: </strong>Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary. In this issue of Cancer Discovery, Darmofal and colleagues describe a new tool that uses information from a clinical sequencing panel to diagnose tumor type, and show that the model is particularly robust. See related article by Darmofal et al., p. 1064 (1).</p>\",\"PeriodicalId\":9430,\"journal\":{\"name\":\"Cancer discovery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":29.7000,\"publicationDate\":\"2024-06-03\",\"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-24-0280\",\"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-24-0280","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
A Deep Learning Model for Cancer Type Prediction Sets a New Standard.
Summary: Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary. In this issue of Cancer Discovery, Darmofal and colleagues describe a new tool that uses information from a clinical sequencing panel to diagnose tumor type, and show that the model is particularly robust. See related article by Darmofal et al., p. 1064 (1).
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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