Prediction of MHC class II binding peptides based on an iterative learning model.

ABSTRACT:

Background

Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides.

Results

A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of pr

SUBMITTER: Murugan N 

PROVIDER: S-EPMC1325229 | biostudies-literature | 2005 Dec

REPOSITORIES: biostudies-literature