SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction.
Identifieur interne : 001E21 ( PubMed/Checkpoint ); précédent : 001E20; suivant : 001E22SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction.
Auteurs : Lawrence J K. Wee [Singapour] ; Diane Simarmata ; Yiu-Wing Kam ; Lisa F P. Ng ; Joo Chuan TongSource :
- BMC genomics [ 1471-2164 ] ; 2010.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Antigens, Peptides.
- chemical , immunology : Antigens, Epitopes, B-Lymphocyte, Peptides.
- Algorithms, Bayes Theorem, Benchmarking, Computer Simulation, Internet, Predictive Value of Tests.
Abstract
The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply.
DOI: 10.1186/1471-2164-11-S4-S21
PubMed: 21143805
Affiliations:
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Ng</name></author><author><name sortKey="Tong, Joo Chuan" sort="Tong, Joo Chuan" uniqKey="Tong J" first="Joo Chuan" last="Tong">Joo Chuan Tong</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2010">2010</date><idno type="RBID">pubmed:21143805</idno><idno type="pmid">21143805</idno><idno type="doi">10.1186/1471-2164-11-S4-S21</idno><idno type="wicri:Area/PubMed/Corpus">001F19</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001F19</idno><idno type="wicri:Area/PubMed/Curation">001F19</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001F19</idno><idno type="wicri:Area/PubMed/Checkpoint">001E21</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001E21</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction.</title><author><name sortKey="Wee, Lawrence J K" sort="Wee, Lawrence J K" uniqKey="Wee L" first="Lawrence J K" last="Wee">Lawrence J K. Wee</name><affiliation wicri:level="1"><nlm:affiliation>Singapore Immunology Network, Biopolis, Singapore. lawrence@bic.nus.edu.sg</nlm:affiliation><country xml:lang="fr">Singapour</country><wicri:regionArea>Singapore Immunology Network, Biopolis</wicri:regionArea><wicri:noRegion>Biopolis</wicri:noRegion></affiliation></author><author><name sortKey="Simarmata, Diane" sort="Simarmata, Diane" uniqKey="Simarmata D" first="Diane" last="Simarmata">Diane Simarmata</name></author><author><name sortKey="Kam, Yiu Wing" sort="Kam, Yiu Wing" uniqKey="Kam Y" first="Yiu-Wing" last="Kam">Yiu-Wing Kam</name></author><author><name sortKey="Ng, Lisa F P" sort="Ng, Lisa F P" uniqKey="Ng L" first="Lisa F P" last="Ng">Lisa F P. Ng</name></author><author><name sortKey="Tong, Joo Chuan" sort="Tong, Joo Chuan" uniqKey="Tong J" first="Joo Chuan" last="Tong">Joo Chuan Tong</name></author></analytic><series><title level="j">BMC genomics</title><idno type="eISSN">1471-2164</idno><imprint><date when="2010" type="published">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Antigens (chemistry)</term><term>Antigens (immunology)</term><term>Bayes Theorem</term><term>Benchmarking</term><term>Computer Simulation</term><term>Epitopes, B-Lymphocyte (immunology)</term><term>Internet</term><term>Peptides (chemistry)</term><term>Peptides (immunology)</term><term>Predictive Value of Tests</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Antigènes ()</term><term>Antigènes (immunologie)</term><term>Déterminants antigéniques des lymphocytes B (immunologie)</term><term>Internet</term><term>Peptides ()</term><term>Peptides (immunologie)</term><term>Référenciation</term><term>Simulation numérique</term><term>Théorème de Bayes</term><term>Valeur prédictive des tests</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Antigens</term><term>Peptides</term></keywords><keywords scheme="MESH" type="chemical" qualifier="immunology" xml:lang="en"><term>Antigens</term><term>Epitopes, B-Lymphocyte</term><term>Peptides</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Antigènes</term><term>Déterminants antigéniques des lymphocytes B</term><term>Peptides</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>Benchmarking</term><term>Computer Simulation</term><term>Internet</term><term>Predictive Value of Tests</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Antigènes</term><term>Internet</term><term>Peptides</term><term>Référenciation</term><term>Simulation numérique</term><term>Théorème de Bayes</term><term>Valeur prédictive des tests</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">21143805</PMID><DateCompleted><Year>2011</Year><Month>03</Month><Day>29</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2164</ISSN><JournalIssue CitedMedium="Internet"><Volume>11 Suppl 4</Volume><PubDate><Year>2010</Year><Month>Dec</Month><Day>02</Day></PubDate></JournalIssue><Title>BMC genomics</Title><ISOAbbreviation>BMC Genomics</ISOAbbreviation></Journal><ArticleTitle>SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction.</ArticleTitle><Pagination><MedlinePgn>S21</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2164-11-S4-S21</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wee</LastName><ForeName>Lawrence J K</ForeName><Initials>LJ</Initials><AffiliationInfo><Affiliation>Singapore Immunology Network, Biopolis, Singapore. lawrence@bic.nus.edu.sg</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Simarmata</LastName><ForeName>Diane</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Kam</LastName><ForeName>Yiu-Wing</ForeName><Initials>YW</Initials></Author><Author ValidYN="Y"><LastName>Ng</LastName><ForeName>Lisa F P</ForeName><Initials>LF</Initials></Author><Author ValidYN="Y"><LastName>Tong</LastName><ForeName>Joo Chuan</ForeName><Initials>JC</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2010</Year><Month>12</Month><Day>02</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Genomics</MedlineTA><NlmUniqueID>100965258</NlmUniqueID><ISSNLinking>1471-2164</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000941">Antigens</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D018985">Epitopes, B-Lymphocyte</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D010455">Peptides</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000941" MajorTopicYN="N">Antigens</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName><QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001499" MajorTopicYN="Y">Bayes Theorem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019985" MajorTopicYN="N">Benchmarking</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018985" MajorTopicYN="Y">Epitopes, B-Lymphocyte</DescriptorName><QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020407" MajorTopicYN="N">Internet</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010455" MajorTopicYN="N">Peptides</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName><QualifierName UI="Q000276" 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Ng</name><name sortKey="Simarmata, Diane" sort="Simarmata, Diane" uniqKey="Simarmata D" first="Diane" last="Simarmata">Diane Simarmata</name><name sortKey="Tong, Joo Chuan" sort="Tong, Joo Chuan" uniqKey="Tong J" first="Joo Chuan" last="Tong">Joo Chuan Tong</name></noCountry><country name="Singapour"><noRegion><name sortKey="Wee, Lawrence J K" sort="Wee, Lawrence J K" uniqKey="Wee L" first="Lawrence J K" last="Wee">Lawrence J K. Wee</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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