{"title":"Improved Deep Learning Prediction of TCR-HLA Associations.","authors":"Fumin Li, Si Liu, Wei Sun","doi":"10.1007/s12561-025-09506-4","DOIUrl":null,"url":null,"abstract":"<p><p>Understanding the relationship between T cell receptors (TCRs) and human leukocyte antigens (HLAs) is essential for elucidating immune response specificity, uncovering mechanisms of autoimmunity, and advancing targeted immunotherapies. We have previously developed a deep learning method, DePTH (Deep Learning Prediction of TCR-HLA associations), to predict the association between a TCR and an HLA based on their amino acid sequences. In this work, we evaluated the performance of DePTH in two additional datasets, and investigated the influence of two potential confounding factors: TCR generation probability and the sequence length of CDR3 (Complementarity-Determining Region 3), which is a key region in the antigen-binding site of TCRs. Building on these insights, we combined training data from two datasets to train a new version of DePTH: DePTH 2.0.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13367516/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Biosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12561-025-09506-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding the relationship between T cell receptors (TCRs) and human leukocyte antigens (HLAs) is essential for elucidating immune response specificity, uncovering mechanisms of autoimmunity, and advancing targeted immunotherapies. We have previously developed a deep learning method, DePTH (Deep Learning Prediction of TCR-HLA associations), to predict the association between a TCR and an HLA based on their amino acid sequences. In this work, we evaluated the performance of DePTH in two additional datasets, and investigated the influence of two potential confounding factors: TCR generation probability and the sequence length of CDR3 (Complementarity-Determining Region 3), which is a key region in the antigen-binding site of TCRs. Building on these insights, we combined training data from two datasets to train a new version of DePTH: DePTH 2.0.
期刊介绍：
Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science.
SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.
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