Abstract：
:Accurately predicting peptides binding to major histocompatibility complex (MHC) I molecules is of great importance to immunologists for elucidating the underlying mechanism of immune recognition and facilitating the design of peptide-based vaccine. Various computational methods have been developed for MHC I-peptide binding prediction, and several of them are reported to achieve high accuracy in recent evaluation on benchmark datasets. For attending the machine learning in immunology competition (MLIC) in prediction of human leukocyte antigen (HLA)-binding peptides, we (FudanCS) have made use of ensemble approaches to further improve the prediction performance by integrating the outputs of several leading predictors. Two ensemble approaches, PM and AvgTanh, have been implemented for attending MLIC. AvgTanh and PM achieved the fourth and the seventh out of all 20 submissions in MLIC in terms of the average AUC. In addition, AvgTanh was awarded the winner in the category of HLA-A*0101 of 9-mer. Overall, the competition results validate the effectiveness of ensemble approaches.
journal_name
J Immunol Methodsjournal_title
Journal of immunological methodsauthors
Hu X,Mamitsuka H,Zhu Sdoi
10.1016/j.jim.2010.09.007subject
Has Abstractpub_date
2011-11-30 00:00:00pages
47-52issue
1-2eissn
0022-1759issn
1872-7905pii
S0022-1759(10)00261-9journal_volume
374pub_type
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journal_title：Journal of immunological methods
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journal_title：Journal of immunological methods
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journal_title：Journal of immunological methods
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