{"title":"在单细胞全息研究中利用基础模型的深度学习能力。","authors":"Qin Ma, Yi Jiang, Hao Cheng, Dong Xu","doi":"10.1038/s41580-024-00756-6","DOIUrl":null,"url":null,"abstract":"Foundation models hold great promise for analyzing single-cell omics data, yet various challenges remain that require further advancements. In this Comment, we discuss the progress, limitations and best practices in applying foundation models to interrogate data and improve downstream tasks in single-cell omics. This Comment discusses the progress, limitations and best practices in applying foundation models to single-cell omics data.","PeriodicalId":19051,"journal":{"name":"Nature Reviews Molecular Cell Biology","volume":"25 8","pages":"593-594"},"PeriodicalIF":81.3000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harnessing the deep learning power of foundation models in single-cell omics\",\"authors\":\"Qin Ma, Yi Jiang, Hao Cheng, Dong Xu\",\"doi\":\"10.1038/s41580-024-00756-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Foundation models hold great promise for analyzing single-cell omics data, yet various challenges remain that require further advancements. In this Comment, we discuss the progress, limitations and best practices in applying foundation models to interrogate data and improve downstream tasks in single-cell omics. This Comment discusses the progress, limitations and best practices in applying foundation models to single-cell omics data.\",\"PeriodicalId\":19051,\"journal\":{\"name\":\"Nature Reviews Molecular Cell Biology\",\"volume\":\"25 8\",\"pages\":\"593-594\"},\"PeriodicalIF\":81.3000,\"publicationDate\":\"2024-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Molecular Cell Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.nature.com/articles/s41580-024-00756-6\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Molecular Cell Biology","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41580-024-00756-6","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Harnessing the deep learning power of foundation models in single-cell omics
Foundation models hold great promise for analyzing single-cell omics data, yet various challenges remain that require further advancements. In this Comment, we discuss the progress, limitations and best practices in applying foundation models to interrogate data and improve downstream tasks in single-cell omics. This Comment discusses the progress, limitations and best practices in applying foundation models to single-cell omics data.
期刊介绍：
Nature Reviews Molecular Cell Biology is a prestigious journal that aims to be the primary source of reviews and commentaries for the scientific communities it serves. The journal strives to publish articles that are authoritative, accessible, and enriched with easily understandable figures, tables, and other display items. The goal is to provide an unparalleled service to authors, referees, and readers, and the journal works diligently to maximize the usefulness and impact of each article. Nature Reviews Molecular Cell Biology publishes a variety of article types, including Reviews, Perspectives, Comments, and Research Highlights, all of which are relevant to molecular and cell biologists. The journal's broad scope ensures that the articles it publishes reach the widest possible audience.
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