{"title":"利用机器学习翻译肿瘤依赖关系。","authors":"","doi":"10.1038/s43018-024-00790-5","DOIUrl":null,"url":null,"abstract":"Cancer dependency maps have accelerated the discovery of essential genes and potential drug targets. Here we used machine learning to build translational dependency maps of patients’ tumors and normal tissue biopsies, which identified oncogenes and synthetic lethalities that are predictive of drug responses and patients’ outcomes.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using machine learning to translate tumor dependencies\",\"authors\":\"\",\"doi\":\"10.1038/s43018-024-00790-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cancer dependency maps have accelerated the discovery of essential genes and potential drug targets. Here we used machine learning to build translational dependency maps of patients’ tumors and normal tissue biopsies, which identified oncogenes and synthetic lethalities that are predictive of drug responses and patients’ outcomes.\",\"PeriodicalId\":18885,\"journal\":{\"name\":\"Nature cancer\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":23.5000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.nature.com/articles/s43018-024-00790-5\",\"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":"Nature cancer","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s43018-024-00790-5","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Using machine learning to translate tumor dependencies
Cancer dependency maps have accelerated the discovery of essential genes and potential drug targets. Here we used machine learning to build translational dependency maps of patients’ tumors and normal tissue biopsies, which identified oncogenes and synthetic lethalities that are predictive of drug responses and patients’ outcomes.
期刊介绍：
Cancer is a devastating disease responsible for millions of deaths worldwide. However, many of these deaths could be prevented with improved prevention and treatment strategies. To achieve this, it is crucial to focus on accurate diagnosis, effective treatment methods, and understanding the socioeconomic factors that influence cancer rates.
Nature Cancer aims to serve as a unique platform for sharing the latest advancements in cancer research across various scientific fields, encompassing life sciences, physical sciences, applied sciences, and social sciences. The journal is particularly interested in fundamental research that enhances our understanding of tumor development and progression, as well as research that translates this knowledge into clinical applications through innovative diagnostic and therapeutic approaches. Additionally, Nature Cancer welcomes clinical studies that inform cancer diagnosis, treatment, and prevention, along with contributions exploring the societal impact of cancer on a global scale.
In addition to publishing original research, Nature Cancer will feature Comments, Reviews, News & Views, Features, and Correspondence that hold significant value for the diverse field of cancer research.
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