Predicting MHC-II binding affinity using multiple instance regression

dc.contributor.author EL-Manzalawy, Yasser
dc.contributor.author Dobbs, Drena
dc.contributor.author Honavar, Vasant
dc.contributor.author Dobbs, Drena
dc.contributor.department Computer Science
dc.contributor.department Genetics, Development and Cell Biology
dc.contributor.department Bioinformatics and Computational Biology
dc.date 2018-02-18T05:08:11.000
dc.date.accessioned 2020-06-30T04:01:10Z
dc.date.available 2020-06-30T04:01:10Z
dc.date.copyright Sat Jan 01 00:00:00 UTC 2011
dc.date.issued 2011-01-01
dc.description.abstract <p>Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir.</p>
dc.description.comments <p>This article is from <em>IEEE/ACM Transactions on Computational Biology and Bioinformatics </em>8 (2011): 1067, doi: <a href="https://doi.org/10.1109/TCBB.2010.94" target="_blank">10.1109/TCBB.2010.94</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/gdcb_las_pubs/118/
dc.identifier.articleid 1121
dc.identifier.contextkey 9776951
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath gdcb_las_pubs/118
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/37782
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/gdcb_las_pubs/118/2011_Dobbs_PredictingBinding.pdf|||Fri Jan 14 18:58:37 UTC 2022
dc.source.uri 10.1109/TCBB.2010.94
dc.subject.disciplines Bioinformatics
dc.subject.disciplines Cell and Developmental Biology
dc.subject.disciplines Computational Biology
dc.subject.disciplines Genetics and Genomics
dc.subject.disciplines Molecular Genetics
dc.subject.keywords MHC-II peptide prediction
dc.subject.keywords multiple instance learning
dc.subject.keywords multiple instance regression
dc.title Predicting MHC-II binding affinity using multiple instance regression
dc.type article
dc.type.genre article
dspace.entity.type Publication
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