ArticleIEEE/ACM transactions on computational biology and bioinformatics
Predicting MHC-II binding affinity using multiple instance regression.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Discovering NDM-1 inhibitors using molecular substructure embeddings representations.Journal of integrative bioinformatics · 2023Article
- Bayesian multiple instance regression for modeling immunogenic neoantigens.Statistical methods in medical research · 2020Article
- In Silico Prediction of Linear B-Cell Epitopes on Proteins.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Machine Learning Methods for Predicting HLA-Peptide Binding Activity.Bioinformatics and biology insights · 2015Review
- New Molecules in Babesia gibsoni and their application for diagnosis, vaccine development, and drug discovery.The Korean journal of parasitology · 2014Review
- MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions.BMC genomics · 2014Article
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Authors and funding
3 authors.
Funding
Abstract
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 data sets 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.
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Registered trials
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