{"title":"预测tcr表位识别：我们做得有多好？","authors":"David Gfeller","doi":"10.1016/j.xgen.2025.100975","DOIUrl":null,"url":null,"abstract":"<p><p>Accurate TCR-epitope interaction predictions have the potential to unlock the use of TCR repertoires for diagnostics, TCR discovery, and cross-reactivity predictions. In this issue of Cell Genomics, Drost et al.<sup>1</sup> developed a streamlined framework for benchmarking such predictions. Careful understanding of the strengths and limitations of existing approaches will be instrumental to improve them and expand the scope of TCR-epitope recognition predictions.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"5 8","pages":"100975"},"PeriodicalIF":11.1000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366649/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting TCR-epitope recognition: How good are we?\",\"authors\":\"David Gfeller\",\"doi\":\"10.1016/j.xgen.2025.100975\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Accurate TCR-epitope interaction predictions have the potential to unlock the use of TCR repertoires for diagnostics, TCR discovery, and cross-reactivity predictions. In this issue of Cell Genomics, Drost et al.<sup>1</sup> developed a streamlined framework for benchmarking such predictions. Careful understanding of the strengths and limitations of existing approaches will be instrumental to improve them and expand the scope of TCR-epitope recognition predictions.</p>\",\"PeriodicalId\":72539,\"journal\":{\"name\":\"Cell genomics\",\"volume\":\"5 8\",\"pages\":\"100975\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2025-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366649/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xgen.2025.100975\",\"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":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2025.100975","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Predicting TCR-epitope recognition: How good are we?
Accurate TCR-epitope interaction predictions have the potential to unlock the use of TCR repertoires for diagnostics, TCR discovery, and cross-reactivity predictions. In this issue of Cell Genomics, Drost et al.1 developed a streamlined framework for benchmarking such predictions. Careful understanding of the strengths and limitations of existing approaches will be instrumental to improve them and expand the scope of TCR-epitope recognition predictions.
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