Title

Prediction of HLA-A2 binding peptides using Bayesian network

Authors

Vadim Astakhov¹ and Artem Cherkasov²*

Affiliation

¹Experimental Medicine Program, Department of Medicine, University of British Columbia, Vancouver, Canada ²Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia, 2733 Heather street, Vancouver, BC, Canada V5Z 3J5.

E-mail*

artc@interchange.ubc.ca; * Corresponding author

Article Type

Prediction Model

Date

received October 5, 2005; revised October 10, 2005; accepted October 10, 2005; published online October 11, 2005

Abstract

Prediction of peptides binding to HLA (human leukocyte antigen) finds application in peptide vaccine design. A number of statistical and structural models have been developed in recent years for HLA binding peptide prediction. However, a Bayesian Network (BNT) model is not available. In this study we describe a BNT model for HLA-A2 binding peptide prediction. It has been demonstrated that the BNT model allows up to 99 % accurate identification of the HLA-A2 binding peptides and provides similar prediction accuracy compared to HMM (Hidden Markov Model) and ANN  (Artificial Neural Network). At the same time, it has been shown that the BNT has that advantage that it allows more accurate performance for smaller sets of empirical data compared to the HMM and the ANN methods. When the size of the training set has been reduced to 40% from the original data, the identification of the HLA-A2 binding peptides by the BNT, ANN and HMM methods produced ARoc (area under receiver operating characteristic) values 0.88, 0.85, 0.85 respectively. The results of the work demonstrate certain advantages of using the Bayesian Networks in predicting the HLA binding peptides using smaller datasets.

Keywords

HLA; antigen presentation; peptides; Bayesian networks; machine learning

Citation

Astakhov & Cherkasov, Bioinformation 1(2): 58-63 (2005)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.