A hybrid model for prediction of peptide binding to MHC molecules

Authors:
Ping Zhang;Vladimir Brusic;Kaye Basford
Affiliations:
The University of Queensland, School of Land, Crop and Food Sciences, QLD, Australia;The University of Queensland, School of Land, Crop and Food Sciences, QLD, Australia and Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA;The University of Queensland, School of Land, Crop and Food Sciences, QLD, Australia
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

We propose a hybrid classification system for predicting peptide binding to major histocompatibility complex (MHC) molecules. This system combines Support Vector Machine (SVM) and Stabilized Matrix Method (SMM). Its performance was assessed using ROC analysis, and compared with the individual component methods using statistical tests. The preliminary test on four HLA alleles provided encouraging evidence for the hybrid model. The datasets used for the experiments are publicly accessible and have been benchmarked by other researchers.