Imanol Duran, Cleo L. Bishop, Jesús Gil, Ryan Wallis
{"title":"机器学习方法捕捉细胞衰老异质性的前景。","authors":"Imanol Duran, Cleo L. Bishop, Jesús Gil, Ryan Wallis","doi":"10.1038/s43587-024-00703-2","DOIUrl":null,"url":null,"abstract":"The identification of senescent cells is a long-standing unresolved challenge, owing to their intrinsic heterogeneity and the lack of universal markers. In this Comment, we discuss the recent advent of machine-learning-based approaches to identifying senescent cells by using unbiased, multiparameter morphological assessments, and how these tools can assist future senescence research.","PeriodicalId":94150,"journal":{"name":"Nature aging","volume":null,"pages":null},"PeriodicalIF":17.0000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The promise of machine learning approaches to capture cellular senescence heterogeneity\",\"authors\":\"Imanol Duran, Cleo L. Bishop, Jesús Gil, Ryan Wallis\",\"doi\":\"10.1038/s43587-024-00703-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The identification of senescent cells is a long-standing unresolved challenge, owing to their intrinsic heterogeneity and the lack of universal markers. In this Comment, we discuss the recent advent of machine-learning-based approaches to identifying senescent cells by using unbiased, multiparameter morphological assessments, and how these tools can assist future senescence research.\",\"PeriodicalId\":94150,\"journal\":{\"name\":\"Nature aging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":17.0000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature aging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43587-024-00703-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature aging","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43587-024-00703-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
The promise of machine learning approaches to capture cellular senescence heterogeneity
The identification of senescent cells is a long-standing unresolved challenge, owing to their intrinsic heterogeneity and the lack of universal markers. In this Comment, we discuss the recent advent of machine-learning-based approaches to identifying senescent cells by using unbiased, multiparameter morphological assessments, and how these tools can assist future senescence research.