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This work applies machine learning methods to viral sequence datasets using hemagglutinin sequences only, represented via position-specific scoring matrix and word embedding. Experiments across taxonomic levels compare evaluation metrics to assess algorithm effectiveness. The 5-gramsTransformer model achieves the strongest predictive performance at higher and lower classification levels.",{"@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/dive-into-machine-learning-algorithms-for-influenza-virus-host-prediction-with-hemagglutinin-sequences/291247/",{"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/dive-into-machine-learning-algorithms-for-influenza-virus-host-prediction-with-hemagglutinin-sequences/291247.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is identifying influenza virus origins important?","Question",{"text":112,"@type":113},"It helps prevent the spread of outbreaks by enabling faster, more accurate predictions for viral sequences and their origins.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data representation and biological focus are used in this study?",{"text":117,"@type":113},"Only hemagglutinin sequences are used, represented through a position-specific scoring matrix and word embedding.",{"name":119,"@type":110,"acceptedAnswer":120},"Which algorithm performs best for predicting viral sequence origins?",{"text":121,"@type":113},"The 5-gramsTransformer neural network shows the most effective results across higher and lower classification levels, with high AUCPR, F1, and MCC scores.","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},291247,1789643865,{"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":39,"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":129,"read_time":144,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","arXiv :2207 . 13842v2 [ cs .LG] 28 Oct 2023  \nDive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences  \nYanhua Xua,∗ , Dominik Wojtczaka  \na Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK  \n\n| ARTICLE INFO |  | AB STRACT |\n| --- | --- | --- |\n| Keywords:\u003Cbr>Influenza Virus\u003Cbr>Position-specific Scoring Matrix Transformer\u003Cbr>Convolutional Neural Network Machine Learning |  | Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has been increasing interest in using machine learning algorithms to provide fast and accurate predictions for viral sequences. In this study, real testing data sets and a variety of evaluation metrics were used to evaluate machine learning algorithms at different taxonomic levels. As hemagglutinin is the major protein in the immune response, only hemagglutinin sequences were used and represented by position-specific scoring matrix and word embedding. The results suggest that the 5-gramsTransformer neural network is the most effective algorithm for predicting viral sequence origins, with approximately 99.54% AUCPR, 98.01% 􀁆1 score and 96.60% MCC at a higher classification level, and approximately 94.74% AUCPR, 87.41% 􀁆1 score and 80.79% MCC at a lower classification level. |\n\n1. Introduction  \nInfluenza is an infectious disease that affects up to onefifth of the world’s population each year, although its prevalence tends to be underestimated (Cox and Subbarao, 1999) . In comparison to seasonal influenza pandemics, influenza pandemics occur less frequently, although each such crisis may result in millions of deaths. The influenza epidemic has adversely affected vulnerable individuals with chronic diseases, and the pandemic has affected people of all ages.  \nThe influenza viruses are classified into four types based on their internal ribonucleoprotein: A, B, C, and D. Influenza D virus does not cause disease in humans. The influenza C virus is only infective in humans, however, it is unlikely to lead to epidemics on a large scale. Because of this, seasonal influenza vaccine strains cannot be used to vaccinate against influenza C and D viruses. Seasonal epidemics are primarily caused by influenza A and B viruses. The influenza B virus is only infectious to humans, while the influenza A virus is infectious to humans and animals, and may result in global epidemics (e.g. pandemics) . There are two glycoproteins under the virus envelope that distinguish the influenza A virus subtypes: hemagglutinin (HA) and neuraminidase (NA) . A total of 18 HA subtypes (numbered 1-18) and 11 NA subtypes (numbered 1-11) have been identified so far (Lazniewski et al., 2018) .  \nHA or NA proteins contain antigenic sites that are recognised by the immune system and which inhibit flu infection. These antigenic sites can rapidly alter in order to escape recognition by the immune system. There is a process known as antigenic drift that generates new influenza A, B, and C strains that are not fully recognized by human immune systems, thereby contributing to seasonal influenza.  \n∗Principal corresponding author  \n [Y.XU137@liverpool.ac.uk](Y.XU137@liverpool.ac.uk) (Y. Xu); [D.Wojtczak@liverpool.ac.uk](D.Wojtczak@liverpool.ac.uk) (D. Wojtczak)  \nORCID(s): 0000-0003-1028-9023 (Y. Xu)  \nWhen influenza A virus proteins undergo drastic changes on antigenic sites, this can cause antigenic shifts. Antigenic shifts may result from the reassortment of different viruses within one or more hosts, leading to the emergence of new viruses (Brockwell-Staats et al., 2009) . In the past, several pandemics have been the result of extreme antigenic shifts in which most people were incapable of resisting the novel virus","cbCaiurGUSLKzp0C","https://ap.wps.com/l/cbCaiurGUSLKzp0C","pdf",835714,13,"English","# Introduction\n# Machine Learning Algorithms for Influenza Virus Host Prediction","[{\"question\":\"Why is identifying influenza virus origins important?\",\"answer\":\"It helps prevent the spread of outbreaks by enabling faster, more accurate predictions for viral sequences and their origins.\"},{\"question\":\"What data representation and biological focus are used in this study?\",\"answer\":\"Only hemagglutinin sequences are used, represented through a position-specific scoring matrix and word embedding.\"},{\"question\":\"Which algorithm performs best for predicting viral sequence origins?\",\"answer\":\"The 5-gramsTransformer neural network shows the most effective results across higher and lower classification levels, with high AUCPR, F1, and MCC scores.\"}]","Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences | PDF",33]