Ensemble Technique for Prediction of T-cell Mycobacterium tuberculosis Epitopes.
Identifieur interne : 000583 ( PubMed/Checkpoint ); précédent : 000582; suivant : 000584Ensemble Technique for Prediction of T-cell Mycobacterium tuberculosis Epitopes.
Auteurs : Divya Khanna [Inde] ; Prashant Singh Rana [Inde]Source :
- Interdisciplinary sciences, computational life sciences [ 1867-1462 ] ; 2019.
Descripteurs français
- KwdFr :
- Aire sous la courbe, Algorithmes, Allèles, Apprentissage machine, Biologie informatique, Déterminants antigéniques des lymphocytes B (), Déterminants antigéniques des lymphocytes T (), Humains, Intelligence artificielle, Lymphocytes T (immunologie), Mycobacterium tuberculosis (), Peptides (), Reproductibilité des résultats, Sensibilité et spécificité, Tests diagnostiques courants, Tuberculose (microbiologie).
- MESH :
- immunologie : Lymphocytes T.
- microbiologie : Tuberculose.
- Aire sous la courbe, Algorithmes, Allèles, Apprentissage machine, Biologie informatique, Déterminants antigéniques des lymphocytes B, Déterminants antigéniques des lymphocytes T, Humains, Intelligence artificielle, Mycobacterium tuberculosis, Peptides, Reproductibilité des résultats, Sensibilité et spécificité, Tests diagnostiques courants.
English descriptors
- KwdEn :
- Algorithms, Alleles, Area Under Curve, Artificial Intelligence, Computational Biology, Diagnostic Tests, Routine, Epitopes, B-Lymphocyte (chemistry), Epitopes, T-Lymphocyte (chemistry), Humans, Machine Learning, Mycobacterium tuberculosis (chemistry), Peptides (chemistry), Reproducibility of Results, Sensitivity and Specificity, T-Lymphocytes (immunology), Tuberculosis (microbiology).
- MESH :
- chemical , chemistry : Epitopes, B-Lymphocyte, Epitopes, T-Lymphocyte, Peptides.
- chemistry : Mycobacterium tuberculosis.
- immunology : T-Lymphocytes.
- microbiology : Tuberculosis.
- Algorithms, Alleles, Area Under Curve, Artificial Intelligence, Computational Biology, Diagnostic Tests, Routine, Humans, Machine Learning, Reproducibility of Results, Sensitivity and Specificity.
Abstract
Development of an effective machine-learning model for T-cell Mycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist's time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide is M. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis of M. tuberculosis. In the present study, computational intelligence methods are used to classify T-cell M. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predict M. tuberculosis epitopes of variable length (7-40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.77 sensitivity with repeated k-fold cross-validation having average accuracy of 80.61%. The proposed ensemble model has been validated and compared with NetMHC 2.3, NetMHC 4.0 servers and CTLpred T-cell prediction server.
DOI: 10.1007/s12539-018-0309-0
PubMed: 30406342
Affiliations:
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la courbe</term><term>Algorithmes</term><term>Allèles</term><term>Apprentissage machine</term><term>Biologie informatique</term><term>Déterminants antigéniques des lymphocytes B</term><term>Déterminants antigéniques des lymphocytes T</term><term>Humains</term><term>Intelligence artificielle</term><term>Mycobacterium tuberculosis</term><term>Peptides</term><term>Reproductibilité des résultats</term><term>Sensibilité et spécificité</term><term>Tests diagnostiques courants</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Development of an effective machine-learning model for T-cell Mycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist's time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide is M. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis of M. tuberculosis. In the present study, computational intelligence methods are used to classify T-cell M. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predict M. tuberculosis epitopes of variable length (7-40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.77 sensitivity with repeated k-fold cross-validation having average accuracy of 80.61%. The proposed ensemble model has been validated and compared with NetMHC 2.3, NetMHC 4.0 servers and CTLpred T-cell prediction server.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30406342</PMID><DateCompleted><Year>2020</Year><Month>04</Month><Day>16</Day></DateCompleted><DateRevised><Year>2020</Year><Month>04</Month><Day>16</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1867-1462</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><Issue>4</Issue><PubDate><Year>2019</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Interdisciplinary sciences, computational life sciences</Title><ISOAbbreviation>Interdiscip Sci</ISOAbbreviation></Journal><ArticleTitle>Ensemble Technique for Prediction of T-cell Mycobacterium tuberculosis Epitopes.</ArticleTitle><Pagination><MedlinePgn>611-627</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s12539-018-0309-0</ELocationID><Abstract><AbstractText>Development of an effective machine-learning model for T-cell Mycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist's time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide is M. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis of M. tuberculosis. In the present study, computational intelligence methods are used to classify T-cell M. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predict M. tuberculosis epitopes of variable length (7-40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.77 sensitivity with repeated k-fold cross-validation having average accuracy of 80.61%. The proposed ensemble model has been validated and compared with NetMHC 2.3, NetMHC 4.0 servers and CTLpred T-cell prediction server.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Khanna</LastName><ForeName>Divya</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, 147004, India. divya.khanna@thapar.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rana</LastName><ForeName>Prashant Singh</ForeName><Initials>PS</Initials><AffiliationInfo><Affiliation>Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, 147004, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate 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MajorTopicYN="N">Mycobacterium tuberculosis</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010455" MajorTopicYN="N">Peptides</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012680" MajorTopicYN="N">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013601" MajorTopicYN="N">T-Lymphocytes</DescriptorName><QualifierName UI="Q000276" MajorTopicYN="Y">immunology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014376" MajorTopicYN="N">Tuberculosis</DescriptorName><QualifierName UI="Q000382" MajorTopicYN="N">microbiology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Ensemble model</Keyword><Keyword MajorTopicYN="N">Feature selection</Keyword><Keyword MajorTopicYN="N">Machine-learning models</Keyword><Keyword MajorTopicYN="N">Mycobacterium tuberculosis</Keyword><Keyword MajorTopicYN="N">T-cell epitopes</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>06</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>10</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2018</Year><Month>09</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>11</Month><Day>9</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>4</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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