{"title":"将蛋白质置于背景中。","authors":"Mengzhou Hu, Trey Ideker","doi":"10.1016/j.cels.2024.09.009","DOIUrl":null,"url":null,"abstract":"<p><p>Proteins exhibit cell-type-specific functions and interactions, yet most ways of representing proteins lack any biological or environmental context. To address this gap, recent work by Li et al.<sup>1</sup> introduces PINNACLE, a geometric deep learning approach that generates contextualized representations of proteins by combined analysis of protein interactions and multiorgan single-cell transcriptomics.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Putting proteins in context.\",\"authors\":\"Mengzhou Hu, Trey Ideker\",\"doi\":\"10.1016/j.cels.2024.09.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Proteins exhibit cell-type-specific functions and interactions, yet most ways of representing proteins lack any biological or environmental context. To address this gap, recent work by Li et al.<sup>1</sup> introduces PINNACLE, a geometric deep learning approach that generates contextualized representations of proteins by combined analysis of protein interactions and multiorgan single-cell transcriptomics.</p>\",\"PeriodicalId\":93929,\"journal\":{\"name\":\"Cell systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.cels.2024.09.009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cels.2024.09.009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proteins exhibit cell-type-specific functions and interactions, yet most ways of representing proteins lack any biological or environmental context. To address this gap, recent work by Li et al.1 introduces PINNACLE, a geometric deep learning approach that generates contextualized representations of proteins by combined analysis of protein interactions and multiorgan single-cell transcriptomics.