Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TCBB.2010.94 | Computational Biology and Bioinformatics, IEEE/ACM Transactions |
Keywords | Field | DocType |
bioinformatics,molecular biophysics,organic compounds,regression analysis,MHC-II binding affinity,amino acid,antigen peptide,immune response,major histocompatibility complex class II,multiple instance regression,pathogenesis,vaccine,MHC-II peptide prediction,multiple instance learning,multiple instance regression. | Plasma protein binding,Antigen,Amino acid,Ligand (biochemistry),Computer science,Peptide,Major histocompatibility complex,Artificial intelligence,Molecular biophysics,Bioinformatics,Machine learning,Peptide sequence | Journal |
Volume | Issue | ISSN |
8 | 4 | 1545-5963 |
Citations | PageRank | References |
4 | 0.50 | 41 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yasser El-Manzalawy | 1 | 44 | 5.01 |
| Drena Dobbs | 2 | 423 | 35.43 |
| Vasant Honavar | 3 | 3353 | 468.10 |