Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of Actinobacillus pleuropneumoniae From Whole Genome Sequences.
Identifieur interne : 000204 ( PubMed/Checkpoint ); précédent : 000203; suivant : 000205Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of Actinobacillus pleuropneumoniae From Whole Genome Sequences.
Auteurs : Zhichang Liu [République populaire de Chine] ; Dun Deng [République populaire de Chine] ; Huijie Lu [République populaire de Chine] ; Jian Sun [République populaire de Chine] ; Luchao Lv [République populaire de Chine] ; Shuhong Li [République populaire de Chine] ; Guanghui Peng [République populaire de Chine] ; Xianyong Ma [République populaire de Chine] ; Jiazhou Li [République populaire de Chine] ; Zhenming Li [République populaire de Chine] ; Ting Rong [République populaire de Chine] ; Gang Wang [République populaire de Chine]Source :
- Frontiers in microbiology [ 1664-302X ] ; 2020.
Abstract
Antimicrobial resistance (AMR) is becoming a huge problem in countries all over the world, and new approaches to identifying strains resistant or susceptible to certain antibiotics are essential in fighting against antibiotic-resistant pathogens. Genotype-based machine learning methods showed great promise as a diagnostic tool, due to the increasing availability of genomic datasets and AST phenotypes. In this article, Support Vector Machine (SVM) and Set Covering Machine (SCM) models were used to learn and predict the resistance of the five drugs (Tetracycline, Ampicillin, Sulfisoxazole, Trimethoprim, and Enrofloxacin). The SVM model used the number of co-occurring k-mers between the genome of the isolates and the reference genes to learn and predict the phenotypes of the bacteria to a specific antimicrobial, while the SCM model uses a greedy approach to construct conjunction or disjunction of Boolean functions to find the most concise set of k-mers that allows for accurate prediction of the phenotype. Five-fold cross-validation was performed on the training set of the SVM and SCM model to select the best hyperparameter values to avoid model overfitting. The training accuracy (mean cross-validation score) and the testing accuracy of SVM and SCM models of five drugs were above 90% regardless of the resistant mechanism of which were acquired resistant or point mutation in the chromosome. The results of correlation between the phenotype and the model predictions of the five drugs indicated that both SVM and SCM models could significantly classify the resistant isolates from the sensitive isolates of the bacteria (p < 0.01), and would be used as potential tools in antimicrobial resistance surveillance and clinical diagnosis in veterinary medicine.
DOI: 10.3389/fmicb.2020.00048
PubMed: 32117101
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type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Lv, Luchao" sort="Lv, Luchao" uniqKey="Lv L" first="Luchao" last="Lv">Luchao Lv</name><affiliation wicri:level="3"><nlm:affiliation>National Veterinary Microbiological Drug Resistance Risk Assessment Laboratory, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>National Veterinary Microbiological Drug Resistance Risk Assessment Laboratory, College of Veterinary Medicine, South China Agricultural University, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Li, Shuhong" sort="Li, Shuhong" uniqKey="Li S" first="Shuhong" last="Li">Shuhong Li</name><affiliation wicri:level="3"><nlm:affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Peng, Guanghui" sort="Peng, Guanghui" uniqKey="Peng G" first="Guanghui" last="Peng">Guanghui Peng</name><affiliation wicri:level="3"><nlm:affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Ma, Xianyong" sort="Ma, Xianyong" uniqKey="Ma X" first="Xianyong" last="Ma">Xianyong Ma</name><affiliation wicri:level="3"><nlm:affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Li, Jiazhou" sort="Li, Jiazhou" uniqKey="Li J" first="Jiazhou" last="Li">Jiazhou Li</name><affiliation wicri:level="3"><nlm:affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Li, Zhenming" sort="Li, Zhenming" uniqKey="Li Z" first="Zhenming" last="Li">Zhenming Li</name><affiliation wicri:level="3"><nlm:affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Rong, Ting" sort="Rong, Ting" uniqKey="Rong T" first="Ting" last="Rong">Ting Rong</name><affiliation wicri:level="3"><nlm:affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author><author><name sortKey="Wang, Gang" sort="Wang, Gang" uniqKey="Wang G" first="Gang" last="Wang">Gang Wang</name><affiliation wicri:level="3"><nlm:affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author></analytic><series><title level="j">Frontiers in microbiology</title><idno type="ISSN">1664-302X</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">Antimicrobial resistance (AMR) is becoming a huge problem in countries all over the world, and new approaches to identifying strains resistant or susceptible to certain antibiotics are essential in fighting against antibiotic-resistant pathogens. Genotype-based machine learning methods showed great promise as a diagnostic tool, due to the increasing availability of genomic datasets and AST phenotypes. In this article, Support Vector Machine (SVM) and Set Covering Machine (SCM) models were used to learn and predict the resistance of the five drugs (Tetracycline, Ampicillin, Sulfisoxazole, Trimethoprim, and Enrofloxacin). The SVM model used the number of co-occurring k-mers between the genome of the isolates and the reference genes to learn and predict the phenotypes of the bacteria to a specific antimicrobial, while the SCM model uses a greedy approach to construct conjunction or disjunction of Boolean functions to find the most concise set of k-mers that allows for accurate prediction of the phenotype. Five-fold cross-validation was performed on the training set of the SVM and SCM model to select the best hyperparameter values to avoid model overfitting. The training accuracy (mean cross-validation score) and the testing accuracy of SVM and SCM models of five drugs were above 90% regardless of the resistant mechanism of which were acquired resistant or point mutation in the chromosome. The results of correlation between the phenotype and the model predictions of the five drugs indicated that both SVM and SCM models could significantly classify the resistant isolates from the sensitive isolates of the bacteria (<i>p</i> < 0.01), and would be used as potential tools in antimicrobial resistance surveillance and clinical diagnosis in veterinary medicine.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32117101</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>05</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1664-302X</ISSN><JournalIssue CitedMedium="Print"><Volume>11</Volume><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>Frontiers in microbiology</Title><ISOAbbreviation>Front Microbiol</ISOAbbreviation></Journal><ArticleTitle>Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of <i>Actinobacillus pleuropneumoniae</i> From Whole Genome Sequences.</ArticleTitle><Pagination><MedlinePgn>48</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fmicb.2020.00048</ELocationID><Abstract><AbstractText>Antimicrobial resistance (AMR) is becoming a huge problem in countries all over the world, and new approaches to identifying strains resistant or susceptible to certain antibiotics are essential in fighting against antibiotic-resistant pathogens. Genotype-based machine learning methods showed great promise as a diagnostic tool, due to the increasing availability of genomic datasets and AST phenotypes. In this article, Support Vector Machine (SVM) and Set Covering Machine (SCM) models were used to learn and predict the resistance of the five drugs (Tetracycline, Ampicillin, Sulfisoxazole, Trimethoprim, and Enrofloxacin). The SVM model used the number of co-occurring k-mers between the genome of the isolates and the reference genes to learn and predict the phenotypes of the bacteria to a specific antimicrobial, while the SCM model uses a greedy approach to construct conjunction or disjunction of Boolean functions to find the most concise set of k-mers that allows for accurate prediction of the phenotype. Five-fold cross-validation was performed on the training set of the SVM and SCM model to select the best hyperparameter values to avoid model overfitting. The training accuracy (mean cross-validation score) and the testing accuracy of SVM and SCM models of five drugs were above 90% regardless of the resistant mechanism of which were acquired resistant or point mutation in the chromosome. The results of correlation between the phenotype and the model predictions of the five drugs indicated that both SVM and SCM models could significantly classify the resistant isolates from the sensitive isolates of the bacteria (<i>p</i> < 0.01), and would be used as potential tools in antimicrobial resistance surveillance and clinical diagnosis in veterinary medicine.</AbstractText><CopyrightInformation>Copyright © 2020 Liu, Deng, Lu, Sun, Lv, Li, Peng, Ma, Li, Li, Rong and Wang.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Zhichang</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Deng</LastName><ForeName>Dun</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lu</LastName><ForeName>Huijie</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Jian</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>National Veterinary Microbiological Drug Resistance Risk Assessment Laboratory, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lv</LastName><ForeName>Luchao</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>National Veterinary Microbiological Drug Resistance Risk Assessment Laboratory, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Shuhong</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Peng</LastName><ForeName>Guanghui</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ma</LastName><ForeName>Xianyong</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Jiazhou</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Zhenming</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rong</LastName><ForeName>Ting</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Gang</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Animal Nutrition and Feed Science of Ministry of Agriculture (South China), Guangzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>02</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front Microbiol</MedlineTA><NlmUniqueID>101548977</NlmUniqueID><ISSNLinking>1664-302X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Actinobacillus pleuropneumoniae</Keyword><Keyword MajorTopicYN="N">Set Covering Machine</Keyword><Keyword MajorTopicYN="N">Support Vector Machine</Keyword><Keyword MajorTopicYN="N">antimicrobial resistance</Keyword><Keyword MajorTopicYN="N">genomics</Keyword><Keyword MajorTopicYN="N">machine learning</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>05</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>01</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32117101</ArticleId><ArticleId IdType="doi">10.3389/fmicb.2020.00048</ArticleId><ArticleId IdType="pmc">PMC7016212</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Antimicrob Agents Chemother. 2013 Jul;57(7):3348-57</Citation><ArticleIdList><ArticleId IdType="pubmed">23650175</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Vet Microbiol. 2010 May 19;142(3-4):309-12</Citation><ArticleIdList><ArticleId 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IdType="pubmed">30369920</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Guangdong</li></region><settlement><li>Jiangmen</li></settlement></list><tree><country name="République populaire de Chine"><region name="Guangdong"><name sortKey="Liu, Zhichang" sort="Liu, Zhichang" uniqKey="Liu Z" first="Zhichang" last="Liu">Zhichang Liu</name></region><name sortKey="Deng, Dun" sort="Deng, Dun" uniqKey="Deng D" first="Dun" last="Deng">Dun Deng</name><name sortKey="Li, Jiazhou" sort="Li, Jiazhou" uniqKey="Li J" first="Jiazhou" last="Li">Jiazhou Li</name><name sortKey="Li, Shuhong" sort="Li, Shuhong" uniqKey="Li S" first="Shuhong" last="Li">Shuhong Li</name><name sortKey="Li, Zhenming" sort="Li, Zhenming" uniqKey="Li Z" first="Zhenming" last="Li">Zhenming Li</name><name sortKey="Lu, Huijie" sort="Lu, Huijie" uniqKey="Lu H" first="Huijie" last="Lu">Huijie Lu</name><name sortKey="Lv, Luchao" sort="Lv, Luchao" uniqKey="Lv L" first="Luchao" last="Lv">Luchao Lv</name><name sortKey="Ma, Xianyong" sort="Ma, Xianyong" uniqKey="Ma X" first="Xianyong" last="Ma">Xianyong Ma</name><name sortKey="Peng, Guanghui" sort="Peng, Guanghui" uniqKey="Peng G" first="Guanghui" last="Peng">Guanghui Peng</name><name sortKey="Rong, Ting" sort="Rong, Ting" uniqKey="Rong T" first="Ting" last="Rong">Ting Rong</name><name sortKey="Sun, Jian" sort="Sun, Jian" uniqKey="Sun J" first="Jian" last="Sun">Jian Sun</name><name sortKey="Wang, Gang" sort="Wang, Gang" uniqKey="Wang G" first="Gang" last="Wang">Gang Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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