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Ensemble approaches for improving HLA Class I-peptide binding prediction
- Source :
- Journal of Immunological Methods. 374:47-52
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
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.
- Subjects :
- biology
Computer science
Histocompatibility Antigens Class I
Immunology
Peptide binding
Human leukocyte antigen
Computational biology
Major histocompatibility complex
Bioinformatics
Class (biology)
Immune recognition
Artificial Intelligence
biology.protein
Humans
Immunology and Allergy
Databases, Protein
Oligopeptides
HLA-A1 Antigen
Subjects
Details
- ISSN :
- 00221759
- Volume :
- 374
- Database :
- OpenAIRE
- Journal :
- Journal of Immunological Methods
- Accession number :
- edsair.doi.dedup.....89ac1e75d4e2ceabcaf8cc1b3ff3c42c