Prediction of Peptides Binding to Major Histocompatibility Class II Molecules Using Machine Learning Methods ()
ABSTRACT
In daily life,we are frequently
attacked by infection organisms such as bacteria and viruses. Major Histocompatibility
(MHC) molecules have an essential role in T-cell activation and initiating an adaptive
immune response. Development of methods for prediction of MHC-Peptide binding is
important in vaccine design and immunotherapy. In this study, we try to predict
the binding between peptides and MHC class II. Support vector machine (SVM) and
Multi-Layer Percep-tron (MLP) are used for classification. These classifiers
based on pseudo amino acid compositions of data that we ex-tracted from PseAAC
server, classify the data. Since, the dataset, used in this work, is imbalanced,
we apply a pre-processing step to over-sample the minority class and come
over this problem. The results show that using the concept of pseudo amino acid
composition and applying over-sampling method, increases the performance of predictor.
Fur-thermore,
the results demonstrate that using the concept of PseAAC and SVM is a successful
method for the prediction of MHC class II molecules.
Share and Cite:
Faramarzi, F. , Beigi, M. , Botorabi, Y. and Mousavi, N. (2013) Prediction of Peptides Binding to Major Histocompatibility Class II Molecules Using Machine Learning Methods.
Engineering,
5, 513-517. doi:
10.4236/eng.2013.510B105.