Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.
Identifieur interne : 000025 ( PubMed/Checkpoint ); précédent : 000024; suivant : 000026Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.
Auteurs : Jing Li [République populaire de Chine] ; Sen Zhang [République populaire de Chine] ; Bo Li [République populaire de Chine] ; Yi Hu [République populaire de Chine] ; Xiao-Ping Kang [République populaire de Chine] ; Xiao-Yan Wu [République populaire de Chine] ; Meng-Ting Huang [République populaire de Chine] ; Yu-Chang Li [République populaire de Chine] ; Zhong-Peng Zhao [République populaire de Chine] ; Cheng-Feng Qin [République populaire de Chine] ; Tao Jiang [République populaire de Chine]Source :
- Molecular biology and evolution [ 1537-1719 ] ; 2020.
Abstract
Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs involved in the next pandemic are currently unpredictable. We aim to build machine learning (ML) models to predict human-adaptive IAV nucleotide composition. A total of 217,549 IAV full-length coding sequences of the PB2 (polymerase basic protein-2), PB1, PA (polymerase acidic protein), HA (hemagglutinin), NP (nucleoprotein), and NA (neuraminidase) segments were decomposed for their codon position-based mononucleotides (12 nts) and dinucleotides (48 dnts). A total of 68,742 human sequences and 68,739 avian sequences (1:1) were resampled to characterize the human adaptation-associated (d)nts with principal component analysis (PCA) and other ML models. Then, the human adaptation of IAV sequences was predicted based on the characterized (d)nts. Respectively, 9, 12, 11, 13, 10 and 9 human-adaptive (d)nts were optimized for the six segments. PCA and hierarchical clustering analysis revealed the linear separability of the optimized (d)nts between the human-adaptive and avian-adaptive sets. The results of the confusion matrix and the area under the receiver operating characteristic curve indicated a high performance of the ML models to predict human adaptation of IAVs. Our model performed well in predicting the human adaptation of the swine/avian IAVs before and after the 2009 H1N1 pandemic. In conclusion, we identified the human adaptation-associated genomic composition of IAV segments. ML models for IAV human adaptation prediction using large IAV genomic data sets can facilitate the identification of key viral factors that affect virus transmission/pathogenicity. Most importantly, it allows the prediction of pandemic influenza.
DOI: 10.1093/molbev/msz276
PubMed: 31750915
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Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:31750915</idno><idno type="pmid">31750915</idno><idno type="doi">10.1093/molbev/msz276</idno><idno type="wicri:Area/PubMed/Corpus">000074</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000074</idno><idno type="wicri:Area/PubMed/Curation">000074</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000074</idno><idno type="wicri:Area/PubMed/Checkpoint">000025</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000025</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.</title><author><name sortKey="Li, Jing" sort="Li, Jing" uniqKey="Li J" first="Jing" last="Li">Jing Li</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Zhang, Sen" sort="Zhang, Sen" uniqKey="Zhang S" first="Sen" last="Zhang">Sen Zhang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Li, Bo" sort="Li, Bo" uniqKey="Li B" first="Bo" last="Li">Bo Li</name><affiliation wicri:level="3"><nlm:affiliation>Department of Clinical Laboratory, the Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Clinical Laboratory, the Fifth Medical Centre of Chinese PLA General Hospital, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Hu, Yi" sort="Hu, Yi" uniqKey="Hu Y" first="Yi" last="Hu">Yi Hu</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Kang, Xiao Ping" sort="Kang, Xiao Ping" uniqKey="Kang X" first="Xiao-Ping" last="Kang">Xiao-Ping Kang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Wu, Xiao Yan" sort="Wu, Xiao Yan" uniqKey="Wu X" first="Xiao-Yan" last="Wu">Xiao-Yan Wu</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Huang, Meng Ting" sort="Huang, Meng Ting" uniqKey="Huang M" first="Meng-Ting" last="Huang">Meng-Ting Huang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Li, Yu Chang" sort="Li, Yu Chang" uniqKey="Li Y" first="Yu-Chang" last="Li">Yu-Chang Li</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Zhao, Zhong Peng" sort="Zhao, Zhong Peng" uniqKey="Zhao Z" first="Zhong-Peng" last="Zhao">Zhong-Peng Zhao</name><affiliation wicri:level="3"><nlm:affiliation>Department of Infection and Immunology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Infection and Immunology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Qin, Cheng Feng" sort="Qin, Cheng Feng" uniqKey="Qin C" first="Cheng-Feng" last="Qin">Cheng-Feng Qin</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Jiang, Tao" sort="Jiang, Tao" uniqKey="Jiang T" first="Tao" last="Jiang">Tao Jiang</name><affiliation wicri:level="3"><nlm:affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author></analytic><series><title level="j">Molecular biology and evolution</title><idno type="eISSN">1537-1719</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs involved in the next pandemic are currently unpredictable. We aim to build machine learning (ML) models to predict human-adaptive IAV nucleotide composition. A total of 217,549 IAV full-length coding sequences of the PB2 (polymerase basic protein-2), PB1, PA (polymerase acidic protein), HA (hemagglutinin), NP (nucleoprotein), and NA (neuraminidase) segments were decomposed for their codon position-based mononucleotides (12 nts) and dinucleotides (48 dnts). A total of 68,742 human sequences and 68,739 avian sequences (1:1) were resampled to characterize the human adaptation-associated (d)nts with principal component analysis (PCA) and other ML models. Then, the human adaptation of IAV sequences was predicted based on the characterized (d)nts. Respectively, 9, 12, 11, 13, 10 and 9 human-adaptive (d)nts were optimized for the six segments. PCA and hierarchical clustering analysis revealed the linear separability of the optimized (d)nts between the human-adaptive and avian-adaptive sets. The results of the confusion matrix and the area under the receiver operating characteristic curve indicated a high performance of the ML models to predict human adaptation of IAVs. Our model performed well in predicting the human adaptation of the swine/avian IAVs before and after the 2009 H1N1 pandemic. In conclusion, we identified the human adaptation-associated genomic composition of IAV segments. ML models for IAV human adaptation prediction using large IAV genomic data sets can facilitate the identification of key viral factors that affect virus transmission/pathogenicity. Most importantly, it allows the prediction of pandemic influenza.</div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">31750915</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>27</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1537-1719</ISSN><JournalIssue CitedMedium="Internet"><Volume>37</Volume><Issue>4</Issue><PubDate><Year>2020</Year><Month>Apr</Month><Day>01</Day></PubDate></JournalIssue><Title>Molecular biology and evolution</Title><ISOAbbreviation>Mol. Biol. Evol.</ISOAbbreviation></Journal><ArticleTitle>Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.</ArticleTitle><Pagination><MedlinePgn>1224-1236</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/molbev/msz276</ELocationID><Abstract><AbstractText>Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs involved in the next pandemic are currently unpredictable. We aim to build machine learning (ML) models to predict human-adaptive IAV nucleotide composition. A total of 217,549 IAV full-length coding sequences of the PB2 (polymerase basic protein-2), PB1, PA (polymerase acidic protein), HA (hemagglutinin), NP (nucleoprotein), and NA (neuraminidase) segments were decomposed for their codon position-based mononucleotides (12 nts) and dinucleotides (48 dnts). A total of 68,742 human sequences and 68,739 avian sequences (1:1) were resampled to characterize the human adaptation-associated (d)nts with principal component analysis (PCA) and other ML models. Then, the human adaptation of IAV sequences was predicted based on the characterized (d)nts. Respectively, 9, 12, 11, 13, 10 and 9 human-adaptive (d)nts were optimized for the six segments. PCA and hierarchical clustering analysis revealed the linear separability of the optimized (d)nts between the human-adaptive and avian-adaptive sets. The results of the confusion matrix and the area under the receiver operating characteristic curve indicated a high performance of the ML models to predict human adaptation of IAVs. Our model performed well in predicting the human adaptation of the swine/avian IAVs before and after the 2009 H1N1 pandemic. In conclusion, we identified the human adaptation-associated genomic composition of IAV segments. ML models for IAV human adaptation prediction using large IAV genomic data sets can facilitate the identification of key viral factors that affect virus transmission/pathogenicity. Most importantly, it allows the prediction of pandemic influenza.</AbstractText><CopyrightInformation>© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Jing</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Sen</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Bo</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Clinical Laboratory, the Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hu</LastName><ForeName>Yi</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kang</LastName><ForeName>Xiao-Ping</ForeName><Initials>XP</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Xiao-Yan</ForeName><Initials>XY</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Meng-Ting</ForeName><Initials>MT</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Graduate School, Anhui Medical University, Hefei, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Yu-Chang</ForeName><Initials>YC</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Zhong-Peng</ForeName><Initials>ZP</Initials><AffiliationInfo><Affiliation>Department of Infection and Immunology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qin</LastName><ForeName>Cheng-Feng</ForeName><Initials>CF</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Tao</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Graduate School, Anhui Medical University, Hefei, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Mol Biol Evol</MedlineTA><NlmUniqueID>8501455</NlmUniqueID><ISSNLinking>0737-4038</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">dinucleotide</Keyword><Keyword MajorTopicYN="N">genomic nucleotide composition</Keyword><Keyword MajorTopicYN="N">human adaptation</Keyword><Keyword MajorTopicYN="N">influenza A viruses (IAVs)</Keyword><Keyword MajorTopicYN="N">machine learning (ML)</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>11</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>11</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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