[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-330281-105":59,"doc-detail-330281-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","deeplion2-deep-multi-instance-contrastive-learning-framework-enhancing-the-prediction-of-cancer-associated-t-cell-receptors","DeepLION2 - Deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors","","DeepLION2 presents a deep multi-instance contrastive learning framework for predicting cancer-associated T cell receptors from TCR repertoire sequencing data. The method models inter-TCR correlations using content-based sparse self-attention that focuses on the top k related TCRs for each TCR. It further applies contrastive learning to bootstrap attention-matrix updates, reducing overfitting to non-cancer-associated signals. Extensive experiments across more than ten cancer types show consistent gains in accuracy, sensitivity, specificity, MCC, and AUC, while also identifying cancer-associated TCRs and their key motifs.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/deeplion2-deep-multi-instance-contrastive-learning-framework-enhancing-the-prediction-of-cancer-associated-t-cell-receptors/330281/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/deeplion2-deep-multi-instance-contrastive-learning-framework-enhancing-the-prediction-of-cancer-associated-t-cell-receptors/330281.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does DeepLION2 address in cancer-associated TCR prediction?","Question",{"text":112,"@type":113},"It targets limitations of existing methods that inadequately model correlations among TCRs in a repertoire and that face sparsity in the distribution of cancer-associated TCRs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does DeepLION2 model relationships between TCRs?",{"text":117,"@type":113},"It uses content-based sparse self-attention, focusing on the top k related TCRs for each TCR to capture inter-TCR correlations.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the purpose of the contrastive learning strategy in DeepLION2?",{"text":121,"@type":113},"It bootstraps parameter updates of the attention matrix, helping prevent the model from fixating on TCRs that are not associated with cancer.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},330281,1790509733,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","TYPE Original Research PUBLISHED 07 March 2024  \nDOI 10.3389/fimmu.2024.1345586  \nOPEN ACCESS  \nEDITED BY Yi Shi,  \nShanghai Jiao Tong University, China  \nREVIEWED BY  \nScott Christley,  \nUniversity of Texas Southwestern Medical Center, United States  \nChong Chu,  \nHarvard Medical School, United States  \n*CORRESPONDENCE Jiayin Wang  \n [wangjiayin@mail.xjtu.edu.cn](wangjiayin@mail.xjtu.edu.cn)[ ](wangjiayin@mail.xjtu.edu.cn)Tao Wang  \n [tddocwangt@163.com](tddocwangt@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 28 November 2023  \nACCEPTED 19 February 2024  \nPUBLISHED 07 March 2024  \nCITATION  \nQian X, Yang G, Li F, Zhang X, Zhu X, Lai X, Xiao X, Wang T and Wang J (2024)  \nDeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs.  \nFront. Immunol. 15:1345586 .  \ndoi: 10.3389/fimmu.2024.1345586  \nCOPYRIGHT  \n© 2024 Qian, Yang, Li, Zhang, Zhu, Lai, Xiao, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention  \nstrategy on motifs  \nXinyang Qian 1,2†, Guang Yang 3†, Fan Li 1,2, Xuanping Zhang 1,2, Xiaoyan Zhu 1,2, Xin Lai 1,2, Xiao Xiao 4,  \nTao Wang 5* and Jiayin Wang 1,2*  \n1School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China, 2Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China, 3 Department of Clinical Oncology, The Second Affiliated Hospital of Air Force Medical University, Xi’an, China, 4Genomics Institute, Geneplus-Shenzhen, Shenzhen, China, 5 Department of Thoracic Surgery, The Second Affiliated Hospital of Air Force Medical University, Xi ’an, China  \nIntroduction: T cell receptor (TCR) repertoires provide valuable insights into complex human diseases, including cancers. Recent advancements in immune sequencing technology have significantly improved our understanding of TCR repertoire. Some computational methods have been devised to identify cancerassociated TCRs and enable cancer detection using TCR sequencing data. However, the existing methods are often limited by their inadequate consideration of the correlations among TCRs within a repertoire, hindering the identification of crucial TCRs. Additionally, the sparsity of cancer-associated TCR distribution presents a challenge in accurate prediction.  \nMethods: To address these issues, we presented DeepLION2, an innovative deep multi-instance contrastive learning framework specifically designed to enhance cancer-associated TCR prediction. DeepLION2 leveraged content-based sparse self-attention, focusing on the top k related TCRs for each TCR, to effectively model inter-TCR correlations. Furthermore, it adopted a contrastive learning strategy for bootstrapping parameter updates of the attention matrix, preventing the model from fixating on non-cancer-associated TCRs.  \nResults: Extensive experimentation on diverse patient cohorts, encompassing over ten cancer types, demonstrated that DeepLION2 significantly outperformed current state-of-the-art methods in terms of accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the curve (AUC) . Notably, DeepLION2 achieved impressive AUC values of 0.933, 0.880, and 0.763 on thyroid, lung, and gastrointestinal cancer cohorts, respectively. Furthermore, it effectively identified cancer-associated TCRs along with their key motifs, highlighting the amino acids that ","cbCain2VOT3mPoTv","https://ap.wps.com/l/cbCain2VOT3mPoTv","pdf",2184935,14,"English","# Introduction\n## Methods\n## Results\n## Conclusion\n# Keywords\n# 1 Introduction","[{\"question\":\"What problem does DeepLION2 address in cancer-associated TCR prediction?\",\"answer\":\"It targets limitations of existing methods that inadequately model correlations among TCRs in a repertoire and that face sparsity in the distribution of cancer-associated TCRs.\"},{\"question\":\"How does DeepLION2 model relationships between TCRs?\",\"answer\":\"It uses content-based sparse self-attention, focusing on the top k related TCRs for each TCR to capture inter-TCR correlations.\"},{\"question\":\"What is the purpose of the contrastive learning strategy in DeepLION2?\",\"answer\":\"It bootstraps parameter updates of the attention matrix, helping prevent the model from fixating on TCRs that are not associated with cancer.\"}]","DeepLION2 - Deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors | PDF",1789987976,35]