Loading…

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool

Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus...

Full description

Saved in:
Bibliographic Details
Published in:BMC bioinformatics 2024-11, Vol.25 (1), p.351-13, Article 351
Main Authors: Beltrán, Jorge F, Belén, Lisandra Herrera, Yáñez, Alejandro J, Jimenez, Luis
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c479t-b2533eba600da9d19c9377761128b196975760f4a701fe8f0692e3445a17d7583
container_end_page 13
container_issue 1
container_start_page 351
container_title BMC bioinformatics
container_volume 25
creator Beltrán, Jorge F
Belén, Lisandra Herrera
Yáñez, Alejandro J
Jimenez, Luis
description Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.
doi_str_mv 10.1186/s12859-024-05972-7
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_13cff1ec5b954ef29f640873577dae3f</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A815377042</galeid><doaj_id>oai_doaj_org_article_13cff1ec5b954ef29f640873577dae3f</doaj_id><sourcerecordid>A815377042</sourcerecordid><originalsourceid>FETCH-LOGICAL-c479t-b2533eba600da9d19c9377761128b196975760f4a701fe8f0692e3445a17d7583</originalsourceid><addsrcrecordid>eNptkktv1DAUhSMEoqXwB1igSGxgkWI7dhyzQVXFY6RKIB5ry-Ncz3iU2IPtjOi_506nlA5CXti-_u5J7tGpqueUnFPad28yZb1QDWG8IUJJ1sgH1SnlkjaMEvHw3vmkepLzhhAqeyIeVyetEozh7bQKXxIM3hYfVvXOJzPW2xQL-JDrsjalhp0ZAI9Q-xBMwW2a5gB1vs4Fpre1qSdj1x4rI5gUUKZZmgzDgYs-uJgmU7xFvRjHp9UjZ8YMz273s-rHh_ffLz81V58_Li4vrhrLpSrNkom2haXpCBmMGqiyqpVSdhQnXlLVKSlkRxw3klAHvSOdYtByLgyVgxR9e1YtDrpDNBu9TX4y6VpH4_VNIaaVNgn_agRNW-scBSuWSnBwTLmOk162QsrBQOtQ691BazsvJxgshII-HYkevwS_1qu405QKQQRTqPDqViHFnzPkoiefLYyjCRDnrFscS3LJKEf05T_oJs4poFd7quOUcdr_pVYGJ9ibjB-2e1F90VOBXhHOkDr_D4VrgMnbGMB5rB81vD5qQKbAr7Iyc8568e3rMcsOrE0x5wTuzhBK9D6e-hBPjfHUN_HUEpte3LfyruVPHtvf4gjeZA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3126412418</pqid></control><display><type>article</type><title>Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool</title><source>Publicly Available Content Database</source><source>PubMed Central</source><source>Coronavirus Research Database</source><creator>Beltrán, Jorge F ; Belén, Lisandra Herrera ; Yáñez, Alejandro J ; Jimenez, Luis</creator><creatorcontrib>Beltrán, Jorge F ; Belén, Lisandra Herrera ; Yáñez, Alejandro J ; Jimenez, Luis</creatorcontrib><description>Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.</description><identifier>ISSN: 1471-2105</identifier><identifier>EISSN: 1471-2105</identifier><identifier>DOI: 10.1186/s12859-024-05972-7</identifier><identifier>PMID: 39522017</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Accuracy ; Algorithms ; Amino acid composition ; Amino acids ; Applications programs ; Computational Biology - methods ; Computer applications ; Datasets ; Deep learning ; Discriminant analysis ; Ensemble learning ; Host-virus relationships ; Humans ; Immune Evasion ; Immune response ; Immune system ; Immunity, Innate - immunology ; Immunoinformatics ; Immunological research ; Infections ; Innate immunity ; Learning algorithms ; Machine Learning ; Neural networks ; Pathogenesis ; Pathogens ; Performance measurement ; Physiological aspects ; Protein ; Proteins ; Python ; Sensitivity analysis ; Software ; Support Vector Machine ; Support vector machines ; Therapeutic targets ; Viral infections ; Viral proteins ; Viral Proteins - chemistry ; Viral Proteins - immunology ; Virology ; Virus ; Viruses</subject><ispartof>BMC bioinformatics, 2024-11, Vol.25 (1), p.351-13, Article 351</ispartof><rights>2024. The Author(s).</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><rights>2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2024 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c479t-b2533eba600da9d19c9377761128b196975760f4a701fe8f0692e3445a17d7583</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11550529/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3126412418?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,38516,43895,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39522017$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Beltrán, Jorge F</creatorcontrib><creatorcontrib>Belén, Lisandra Herrera</creatorcontrib><creatorcontrib>Yáñez, Alejandro J</creatorcontrib><creatorcontrib>Jimenez, Luis</creatorcontrib><title>Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool</title><title>BMC bioinformatics</title><addtitle>BMC Bioinformatics</addtitle><description>Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Amino acid composition</subject><subject>Amino acids</subject><subject>Applications programs</subject><subject>Computational Biology - methods</subject><subject>Computer applications</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Discriminant analysis</subject><subject>Ensemble learning</subject><subject>Host-virus relationships</subject><subject>Humans</subject><subject>Immune Evasion</subject><subject>Immune response</subject><subject>Immune system</subject><subject>Immunity, Innate - immunology</subject><subject>Immunoinformatics</subject><subject>Immunological research</subject><subject>Infections</subject><subject>Innate immunity</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Neural networks</subject><subject>Pathogenesis</subject><subject>Pathogens</subject><subject>Performance measurement</subject><subject>Physiological aspects</subject><subject>Protein</subject><subject>Proteins</subject><subject>Python</subject><subject>Sensitivity analysis</subject><subject>Software</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Therapeutic targets</subject><subject>Viral infections</subject><subject>Viral proteins</subject><subject>Viral Proteins - chemistry</subject><subject>Viral Proteins - immunology</subject><subject>Virology</subject><subject>Virus</subject><subject>Viruses</subject><issn>1471-2105</issn><issn>1471-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkktv1DAUhSMEoqXwB1igSGxgkWI7dhyzQVXFY6RKIB5ry-Ncz3iU2IPtjOi_506nlA5CXti-_u5J7tGpqueUnFPad28yZb1QDWG8IUJJ1sgH1SnlkjaMEvHw3vmkepLzhhAqeyIeVyetEozh7bQKXxIM3hYfVvXOJzPW2xQL-JDrsjalhp0ZAI9Q-xBMwW2a5gB1vs4Fpre1qSdj1x4rI5gUUKZZmgzDgYs-uJgmU7xFvRjHp9UjZ8YMz273s-rHh_ffLz81V58_Li4vrhrLpSrNkom2haXpCBmMGqiyqpVSdhQnXlLVKSlkRxw3klAHvSOdYtByLgyVgxR9e1YtDrpDNBu9TX4y6VpH4_VNIaaVNgn_agRNW-scBSuWSnBwTLmOk162QsrBQOtQ691BazsvJxgshII-HYkevwS_1qu405QKQQRTqPDqViHFnzPkoiefLYyjCRDnrFscS3LJKEf05T_oJs4poFd7quOUcdr_pVYGJ9ibjB-2e1F90VOBXhHOkDr_D4VrgMnbGMB5rB81vD5qQKbAr7Iyc8568e3rMcsOrE0x5wTuzhBK9D6e-hBPjfHUN_HUEpte3LfyruVPHtvf4gjeZA</recordid><startdate>20241109</startdate><enddate>20241109</enddate><creator>Beltrán, Jorge F</creator><creator>Belén, Lisandra Herrera</creator><creator>Yáñez, Alejandro J</creator><creator>Jimenez, Luis</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20241109</creationdate><title>Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool</title><author>Beltrán, Jorge F ; Belén, Lisandra Herrera ; Yáñez, Alejandro J ; Jimenez, Luis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c479t-b2533eba600da9d19c9377761128b196975760f4a701fe8f0692e3445a17d7583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Amino acid composition</topic><topic>Amino acids</topic><topic>Applications programs</topic><topic>Computational Biology - methods</topic><topic>Computer applications</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Discriminant analysis</topic><topic>Ensemble learning</topic><topic>Host-virus relationships</topic><topic>Humans</topic><topic>Immune Evasion</topic><topic>Immune response</topic><topic>Immune system</topic><topic>Immunity, Innate - immunology</topic><topic>Immunoinformatics</topic><topic>Immunological research</topic><topic>Infections</topic><topic>Innate immunity</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Neural networks</topic><topic>Pathogenesis</topic><topic>Pathogens</topic><topic>Performance measurement</topic><topic>Physiological aspects</topic><topic>Protein</topic><topic>Proteins</topic><topic>Python</topic><topic>Sensitivity analysis</topic><topic>Software</topic><topic>Support Vector Machine</topic><topic>Support vector machines</topic><topic>Therapeutic targets</topic><topic>Viral infections</topic><topic>Viral proteins</topic><topic>Viral Proteins - chemistry</topic><topic>Viral Proteins - immunology</topic><topic>Virology</topic><topic>Virus</topic><topic>Viruses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Beltrán, Jorge F</creatorcontrib><creatorcontrib>Belén, Lisandra Herrera</creatorcontrib><creatorcontrib>Yáñez, Alejandro J</creatorcontrib><creatorcontrib>Jimenez, Luis</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Biological Science Collection</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Beltrán, Jorge F</au><au>Belén, Lisandra Herrera</au><au>Yáñez, Alejandro J</au><au>Jimenez, Luis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool</atitle><jtitle>BMC bioinformatics</jtitle><addtitle>BMC Bioinformatics</addtitle><date>2024-11-09</date><risdate>2024</risdate><volume>25</volume><issue>1</issue><spage>351</spage><epage>13</epage><pages>351-13</pages><artnum>351</artnum><issn>1471-2105</issn><eissn>1471-2105</eissn><abstract>Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>39522017</pmid><doi>10.1186/s12859-024-05972-7</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1471-2105
ispartof BMC bioinformatics, 2024-11, Vol.25 (1), p.351-13, Article 351
issn 1471-2105
1471-2105
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_13cff1ec5b954ef29f640873577dae3f
source Publicly Available Content Database; PubMed Central; Coronavirus Research Database
subjects Accuracy
Algorithms
Amino acid composition
Amino acids
Applications programs
Computational Biology - methods
Computer applications
Datasets
Deep learning
Discriminant analysis
Ensemble learning
Host-virus relationships
Humans
Immune Evasion
Immune response
Immune system
Immunity, Innate - immunology
Immunoinformatics
Immunological research
Infections
Innate immunity
Learning algorithms
Machine Learning
Neural networks
Pathogenesis
Pathogens
Performance measurement
Physiological aspects
Protein
Proteins
Python
Sensitivity analysis
Software
Support Vector Machine
Support vector machines
Therapeutic targets
Viral infections
Viral proteins
Viral Proteins - chemistry
Viral Proteins - immunology
Virology
Virus
Viruses
title Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T04%3A26%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20viral%20proteins%20that%20evade%20the%20innate%20immune%20system:%20a%20machine%20learning-based%20immunoinformatics%20tool&rft.jtitle=BMC%20bioinformatics&rft.au=Beltr%C3%A1n,%20Jorge%20F&rft.date=2024-11-09&rft.volume=25&rft.issue=1&rft.spage=351&rft.epage=13&rft.pages=351-13&rft.artnum=351&rft.issn=1471-2105&rft.eissn=1471-2105&rft_id=info:doi/10.1186/s12859-024-05972-7&rft_dat=%3Cgale_doaj_%3EA815377042%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c479t-b2533eba600da9d19c9377761128b196975760f4a701fe8f0692e3445a17d7583%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3126412418&rft_id=info:pmid/39522017&rft_galeid=A815377042&rfr_iscdi=true