Modified association rule mining approach for the MHC-peptide binding problem

Galip Gürkan Yardimci, Alper Küçükural, Yücel Saygin, Uǧur Sezerman

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

2 Scopus citations

Abstract

Computational approach to predict peptide binding to major histocompatibility complex (MHC) is crucial for vaccine design since these peptides can act as a T-Cell epitope to trigger immune response. There are two main branches for peptide prediction methods; structural and data mining approaches. These methods can be successfully used for prediction of T-Cell epitopes in cancer, allergy and infectious diseases. In this paper, association rule mining methods are implemented to generate rules of peptide selection by MHCs. To capture the binding characteristics, modified rule mining and data transformation methods are implemented in this paper. Peptides are known to bind to the same MHC show sequence variability, to capture this characteristic, we used a reduced amino acid alphabet by clustering amino acids according to their physico-chemical properties. Using the classification of amino acids and the OR-operator to combine the rules to reflect that different amino acid types and positions along the peptide may be responsible for binding are the innovations of the method presented. We can predict MHC Class-I binding with 75-97% coverage and 76-100% accuracy.

Original languageEnglish (US)
Title of host publicationComputer and Information Sciences - ISCIS 2006
Subtitle of host publication21th International Symposium, Proceedings
PublisherSpringer-Verlag
Pages165-173
Number of pages9
ISBN (Print)3540472428, 9783540472421
DOIs
StatePublished - 2006
Externally publishedYes
EventISCIS 2006: 21th International Symposium on Computer and Information Sciences - Istanbul, Turkey
Duration: Nov 1 2006Nov 3 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceISCIS 2006: 21th International Symposium on Computer and Information Sciences
CountryTurkey
CityIstanbul
Period11/1/0611/3/06

Keywords

  • Association rule mining
  • Data mining
  • MHC class-I
  • Peptides
  • Reduced amino acid alphabet

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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