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 ENG  Vol.5 No.10 B , October 2013
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.
Cite this paper: 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.
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