[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124711-en":3,"doc-seo-124711-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124711,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Accurately identifying hemagglutinin using sequence information and machine learning methods","Hemagglutinin (HA) enables influenza viral entry and infection by mediating fusion between host membrane and virus. Because HA is critical for influenza-driven disease processes, accurate HA identification supports targeted vaccine and drug development. A computational, machine learning pipeline is proposed to identify HA from sequence information. A benchmark dataset of 106 HA and 106 non-HA sequences is built and encoded with sequence-based features, then optimized and modeled using stacking and four machine learning methods.","TYPE Original Research PUBLISHED 31 October 2023 DOI 10.3389/fmed.2023.1281880  \nOPEN ACCESS  \nEDITED BY  \nJ. Francis Borgio,  \nImam Abdulrahman Bin Faisal University, Saudi Arabia  \nREVIEWED BY  \nQiang Tang,  \nChengdu University of Traditional Chinese Medicine, China  \nJiesi Luo,  \nSouthwest Medical University, China Ke Han,  \nHarbin University of Commerce, China Zhijun Liao,  \nFujian Medical University, China  \n*CORRESPONDENCE  \nXiaolong Yu  \n [yuxiaolong@hainanu.edu.cn](yuxiaolong@hainanu.edu.cn)[ ](yuxiaolong@hainanu.edu.cn)Hao Lin  \n [hlin@uestc.edu.cn](hlin@uestc.edu.cn)[ ](hlin@uestc.edu.cn)Chengbing Huang  \n [20049607@abtu.edu.cn](20049607@abtu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 23 August 2023  \nACCEPTED 16 October 2023  \nPUBLISHED 31 October 2023  \nCITATION  \nZou X, Ren L, Cai P, Zhang Y, Ding H, Deng K, Yu X, Lin H and Huang C (2023) Accurately identifying hemagglutinin using sequence information and machine learning methods. Front. Med. 10:1281880 .  \ndoi: 10.3389/fmed.2023.1281880  \nCOPYRIGHT  \n© 2023 Zou, Ren, Cai, Zhang, Ding, Deng, Yu, Lin and Huang. 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.  \nAccurately identifying hemagglutinin using sequence information and machine learning methods  \nXidan Zou 1†, Liping Ren 2†, Peiling Cai3, Yang Zhang4, Hui Ding 1, Kejun Deng 1, Xiaolong Yu 5*, Hao Lin 1*and Chengbing Huang 6*  \n1School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China, 2School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China, 3School of Basic Medical Sciences, Chengdu University, Chengdu, China, 4 Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China, 5School of Materials Science and Engineering, Hainan University, Haikou, China, 6School of Computer Science and Technology, Aba Teachers University, Aba, China  \nIntroduction: Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA.  \nMethods: In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm.  \nResults and discussion: The model achieved an accuracy of 95. 85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93. 18% and with an area under the ROC curve of 0.9793. The code can be found from [https://github.com/Zouxidan/HA_predict.git](https://github.com/Zouxidan/HA_predict.git. The)[. The](https://github.com/Zouxidan/HA_predict.git. The) proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.  \nKEYWORDS  \nhemagglutinin, machine learning, sequence features, feature extraction, stack","cbCainmJ6HbeAiGL","https://ap.wps.com/l/cbCainmJ6HbeAiGL","pdf",2604930,1,9,"English","en",105,"# Introduction\n# Methods\n# Results and discussion","[{\"question\":\"What is the purpose of identifying hemagglutinin (HA) in this study?\",\"answer\":\"HA is essential for influenza viral entry and infection. The study focuses on building a computational model that can accurately identify HA to support targeted vaccine and drug development.\"},{\"question\":\"How is the dataset for HA identification constructed?\",\"answer\":\"The study builds a benchmark dataset using UniProt, containing 106 HA sequences and 106 non-HA sequences. Sequence-based features are derived to represent samples for modeling.\"},{\"question\":\"Which modeling approach achieves the reported performance?\",\"answer\":\"Features are optimized and fed into four machine learning methods, and an integrated classifier is built using the stacking algorithm. Performance is evaluated with 5-fold cross-validation and an independent test.\"}]","Accurately identifying hemagglutinin using sequence information and machine learning methods | PDF",1785894051,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"accurately-identifying-hemagglutinin-using-sequence-information-and-machine-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/accurately-identifying-hemagglutinin-using-sequence-information-and-machine-learning-methods/124711/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of identifying hemagglutinin (HA) in this study?","Question",{"text":75,"@type":76},"HA is essential for influenza viral entry and infection. The study focuses on building a computational model that can accurately identify HA to support targeted vaccine and drug development.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset for HA identification constructed?",{"text":80,"@type":76},"The study builds a benchmark dataset using UniProt, containing 106 HA sequences and 106 non-HA sequences. Sequence-based features are derived to represent samples for modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling approach achieves the reported performance?",{"text":84,"@type":76},"Features are optimized and fed into four machine learning methods, and an integrated classifier is built using the stacking algorithm. 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