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MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions.

Authors
Type
Published Article
Journal
BMC Genomics
1471-2164
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
15 Suppl 9
Identifiers
DOI: 10.1186/1471-2164-15-S9-S9
PMID: 25521198
Source
Medline
License
Unknown

Abstract

Experimental results on a benchmark dataset have shown that, the performance of MHC2MIL is significantly improved by considering the instances of both 9 and 11 amino acids, as well as by emphasizing amino acids at key positions in the instance. The results are consistent with those reported in the literature on MHC-II peptide binding. In addition to five important positions (1, 4, 6, 7 and 9) for HLA(human leukocyte antigen, the name of MHC in Humans) DR peptide binding, we also find that position 2 may play some roles in the binding process. By using 5-fold cross validation on the benchmark dataset, MHC2MIL outperforms two state-of-the-art methods of MHC2SK and NN-align with being statistically significant, on 12 HLA DP and DQ molecules. In addition, it achieves comparable performance with MHC2SK and NN-align on 14 HLA DR molecules. MHC2MIL is freely available at http://datamining-iip.fudan.edu.cn/service/MHC2MIL/index.html.

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