TITLE:
Prediction of Peptides Binding to Major Histocompatibility Class II Molecules Using Machine Learning Methods
AUTHORS:
Fateme Kazemi Faramarzi, Majid Mohammad Beigi, Yasamin Botorabi, Najme Mousavi
KEYWORDS:
MHC Class II; Imbalanced Data; SMOTE; SVM
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
December
20,
2013
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.