Predicting Peptides Binding to MHC Class II Molecules Using Boosted Decision Tree

Haneen Tartory, Hashem Tamimi and Yaqoub Ashhab
1st Students Innovation Conference

Prediction of MHC class II binding peptides represents a challenging problem in machine learning. Many researchers applied different machine learning tool for the prediction, these tools are: SVM, neural network, genetic programming, and HMM, but each has its own strengths and weaknesses. In this paper we used the Boosted decision tree algorithm for the prediction using two different methods in representing the peptide sequences. The experiments results show that the boosted decision tree algorithm can be developed to give a good algorithm for MHC II prediction problem.

Conference - Poster
Published date: 
Wednesday, June 13, 2012 - 17:15