[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124612-en":3,"doc-seo-124612-105":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},124612,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Using machine learning to detect coronaviruses potentially infectious to humans","Using spike-protein sequences and host-receptor binding annotations, an artificial neural network model is constructed to learn patterns across alpha and beta coronaviruses and generate a Human-Binding Potential (h-BiP) score. The model accurately distinguishes coronaviruses by binding potential and identifies previously unreported human-receptor binders. Molecular dynamics analysis refines binding-property understanding for selected candidates. Re-training while excluding SARS-CoV-2 enables surveillance of novel coronavirus emergence and predicts SARS-CoV-2 human receptor binding, highlighting ML for host-expansion event prediction.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nUsing machine learning to detect coronaviruses potentially infectious to humans  \nPermalink  \n[https://escholarship.org/uc/item/5x3516hh](https://escholarship.org/uc/item/5x3516hh)  \nJournal  \nScientific Reports, 13(1)  \nISSN  \n2045-2322  \nAuthors  \nGonzalez-Isunza, Georgina  \nJawaid, M Zaki Liu, Pengyu et al.  \nPublication Date  \n2023  \nDOI  \n10.1038/s41598-023-35861-7  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nUsing machine learning to detect coronaviruses potentially infectious to humans  \nGeorgina Gonzalez‑Isunza1, M. Zaki Jawaid4, Pengyu Liu1, Daniel L. Cox4, Mariel Vazquez1,3 & Javier Arsuaga2,3*  \nEstablishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non‑human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from spike protein sequences of alpha and beta coronaviruses and their binding annotation to their host receptor. The proposed method produces a human‑Binding Potential (h‑BiP) score that distinguishes, with high accuracy, the binding potential among coronaviruses.  \nThree viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 and Pipistrellus abramus bat coronavirus HKU5‑related (both MERS related viruses), and Rhinolophus affinis coronavirus isolate LYRa3 (a SARS related virus). We further analyze the binding properties of BtCoV/133/2005 and LYRa3 using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re‑trained the model on a set that excludes SARS‑CoV‑2 and all viral sequences released after the SARS‑CoV‑2 was published. The results predict the binding of SARS‑CoV‑2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.  \nMost novel viral human diseases, particularly those that have caused recent epidemics, are known to have originated in non-human animal hosts1–3. Host expansion, the ability of a virus to cross species, is a key step in the evolution of such viruses3–5. COVID-19 is a recent example of a disease caused by a host expansion event that permitted SARS-CoV-2, a SARS-related coronavirus, to propagate from a yet unknown non-human animal to humans5. Alpha and beta coronaviruses affect a wide range of animals interacting with humans, including farm animals and camels, thus facilitating zoonotic transmission6, 7. Moreover, all seven human coronaviruses belong to either the alpha or beta coronavirus genus7. While several studies have confirmed bats and rodents as natural hosts for the alpha and beta coronaviruses affecting humans, there is evidence of intermediate hosts that facilitate evolutionary events, leading to strains that eventually propagate in humans1, 6, 8. Determining which non-human animal viruses may infect humans remains a challenge.  \nExperimental evidence is still the gold standard used to determine whether a virus can infect a host9, 10. However, the complete host range of a virus is often unknown. Recent studies have used diverse in-silico techniques to predict viral hosts and host expansion events, including qualitative expert analysis11, probabilistic12 and machine learning (ML)13–17models.  \nThe problem of host prediction is commonly addressed using similarity analysis of viral genomes, where similar genomes are more likely to share the same hosts10, 18. Host prediction through genome similarity can be achieved by alignment-based or alignment-free approaches17, 19. Compu","cbCainfmKOhJycaF","https://ap.wps.com/l/cbCainfmKOhJycaF","pdf",1573399,1,13,"English","en",105,"# Background and challenge\n## Host expansion and zoonotic origins\n## Limits of experimental and in-silico approaches\n# Computational approaches to host prediction\n## Genome similarity and alignment methods\n## Alignment-free methods and their drawbacks\n# Proposed machine learning framework\n## Predicting spike protein binding to human receptors\n## h-BiP scoring and model training\n## Candidate discovery and validation with molecular dynamics\n## Surveillance use case and SARS-CoV-2 exclusion experiment","[{\"question\":\"What does the proposed h-BiP model predict?\",\"answer\":\"It predicts a Human-Binding Potential (h-BiP) score indicating the binding potential of coronaviruses to a human receptor based on spike protein sequences and binding annotations.\"},{\"question\":\"How are candidate human-receptor binding viruses identified?\",\"answer\":\"The model distinguishes coronaviruses by binding potential and identifies three viruses that had not been previously known to bind human receptors, followed by binding analysis using molecular dynamics for selected cases.\"},{\"question\":\"How is the model tested for surveillance of new coronaviruses?\",\"answer\":\"The model is re-trained on data that excludes SARS-CoV-2 and sequences released after SARS-CoV-2 publication, and it then predicts SARS-CoV-2 binding to a human receptor.\"}]","Using machine learning to detect coronaviruses potentially infectious to humans | PDF",1785893311,33,{"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},"using-machine-learning-to-detect-coronaviruses-potentially-infectious-to-humans","",{"@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/using-machine-learning-to-detect-coronaviruses-potentially-infectious-to-humans/124612/",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 does the proposed h-BiP model predict?","Question",{"text":75,"@type":76},"It predicts a Human-Binding Potential (h-BiP) score indicating the binding potential of coronaviruses to a human receptor based on spike protein sequences and binding annotations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are candidate human-receptor binding viruses identified?",{"text":80,"@type":76},"The model distinguishes coronaviruses by binding potential and identifies three viruses that had not been previously known to bind human receptors, followed by binding analysis using molecular dynamics for selected cases.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model tested for surveillance of new coronaviruses?",{"text":84,"@type":76},"The model is re-trained on data that excludes SARS-CoV-2 and sequences released after SARS-CoV-2 publication, and it then predicts SARS-CoV-2 binding to a human receptor.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]