A hybrid model for prediction of peptide binding to MHC molecules

Ping Zhang, Vladimir Brusic, Kaye Basford

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationAdvances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
Pages529-536
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - Sep 21 2009
Externally publishedYes
Event15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland, New Zealand
Duration: Nov 25 2008Nov 28 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Neuro-Information Processing, ICONIP 2008
CountryNew Zealand
CityAuckland
Period11/25/0811/28/08

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, P., Brusic, V., & Basford, K. (2009). A hybrid model for prediction of peptide binding to MHC molecules. In Advances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers (PART 1 ed., pp. 529-536). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5506 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-02490-0_65