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Authors: Shib Sankar Bhowmick 1 ; Indrajit Saha 2 ; Giovanni Mazzocco 3 ; Ujjwal Maulik 2 ; Luis Rato 4 ; Debotosh Bhattacharjee 2 and Dariusz Plewczynski 3

Affiliations: 1 Jadavpur University and University of Evora, India ; 2 Jadavpur University, India ; 3 University of Warsaw, Poland ; 4 University of Evora, Portugal

ISBN: 978-989-758-012-3

Keyword(s): HLA Class II, Machine Learning, MHC, Peptide Binding, T Cell Epitopes.

Related Ontology Subjects/Areas/Topics: Agents ; Algorithms and Software Tools ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Data Mining and Machine Learning ; Enterprise Information Systems ; Immuno- and Chemo-Informatics ; Information Systems Analysis and Specification ; Methodologies and Technologies ; Operational Research ; Pattern Recognition, Clustering and Classification ; Simulation ; Soft Computing

Abstract: In this article, the recently developed RotaSVM is used for accurate prediction of binding peptides to Human Leukocyte Antigens class II (HLA class II) proteins. The HLA II - peptide complexes are generated in the antigen presenting cells (APC) and transported to the cell membrane to elicit an immune response via T-cell activation. The understanding of HLA class II protein-peptide binding interaction facilitates the design of peptide-based vaccine, where the high rate of polymorphisms in HLA class II molecules poses a big challenge. To determine the binding activity of 636 non-redundant peptides, a set of 27 HLA class II proteins are considered in the present study. The prediction of HLA class II - peptide binding is carried out by an ensemble classifier called RotaSVM. In RotaSVM, the feature selection scheme generates bootstrap samples that are further used to create a diverse set of features using Principal Component Analysis. Thereafter, Support Vector Machines are trained with th ese bootstrap samples with the integration of their original feature values. The effectiveness of the RotaSVM for HLA class II protein-peptide binding prediction is demonstrated in comparison with other traditional classifiers by evaluating several validity measures with the visual plot of ROC curves. Finally, Friedman test is conducted to judge the statistical significance of RotaSVM in prediction of peptides binding to HLA class II proteins. (More)

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Paper citation in several formats:
Bhowmick, S.; Saha, I.; Mazzocco, G.; Maulik, U.; Rato, L.; Bhattacharjee, D. and Plewczynski, D. (2014). Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 178-185. DOI: 10.5220/0004804801780185

@conference{bioinformatics14,
author={Shib Sankar Bhowmick. and Indrajit Saha. and Giovanni Mazzocco. and Ujjwal Maulik. and Luis Rato. and Debotosh Bhattacharjee. and Dariusz Plewczynski.},
title={Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={178-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004804801780185},
isbn={978-989-758-012-3},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction
SN - 978-989-758-012-3
AU - Bhowmick, S.
AU - Saha, I.
AU - Mazzocco, G.
AU - Maulik, U.
AU - Rato, L.
AU - Bhattacharjee, D.
AU - Plewczynski, D.
PY - 2014
SP - 178
EP - 185
DO - 10.5220/0004804801780185

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