{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hla-class-i-binding-prediction-via","title":"HLA class I binding prediction via convolutional neural networks","arxiv_id":"1701.00593","date":"2017-01-03","proceeding":null,"authors":["Yeeleng Scott Vang","Xiaohui Xie"],"abstract":"Many biological processes are governed by protein-ligand interactions. One\nsuch example is the recognition of self and nonself cells by the immune system.\nThis immune response process is regulated by the major histocompatibility\ncomplex (MHC) protein which is encoded by the human leukocyte antigen (HLA)\ncomplex. Understanding the binding potential between MHC and peptides can lead\nto the design of more potent, peptide-based vaccines and immunotherapies for\ninfectious autoimmune diseases.\n  We apply machine learning techniques from the natural language processing\n(NLP) domain to address the task of MHC-peptide binding prediction. More\nspecifically, we introduce a new distributed representation of amino acids,\nname HLA-Vec, that can be used for a variety of downstream proteomic machine\nlearning tasks. We then propose a deep convolutional neural network\narchitecture, name HLA-CNN, for the task of HLA class I-peptide binding\nprediction. Experimental results show combining the new distributed\nrepresentation with our HLA-CNN architecture achieves state-of-the-art results\nin the majority of the latest two Immune Epitope Database (IEDB) weekly\nautomated benchmark datasets. We further apply our model to predict binding on\nthe human genome and identify 15 genes with potential for self binding.","url_abs":"http://arxiv.org/abs/1701.00593v2","url_pdf":"http://arxiv.org/pdf/1701.00593v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hla-class-i-binding-prediction-via","repo_url":"https://github.com/uci-cbcl/HLA-bind","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"mhc-presentation-prediction","task_name":"MHC presentation prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}