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Identification of Relevant Properties for Epitopes Detection Using a Regression Model
November/December 2011 (vol. 8 no. 6)
pp. 1700-1707
Jérôme Ambroise, Université catholique de Louvain, Louvain-la-Neuve
Joachim Giard, Université catholique de Louvain, Louvain-la-Neuve
Jean-Luc Gala, Université catholique de Louvain, Louvain-la-Neuve
Benoit Macq, Université catholique de Louvain, Louvain-la-Neuve
A B-cell epitope is a part of an antigen that is recognized by a specific antibody or B-cell receptor. Detecting the immunogenic region of the antigen is useful in numerous immunodetection and immunotherapeutics applications. The aim of this paper is to find relevant properties to discriminate the location of potential epitopes from the rest of the protein surface. The most relevant properties, identified using two evaluation approaches, are the geometric properties, followed by the conservation score and some chemical properties, such as the proportion of glycine. The selected properties are used in a patch-based epitope localization method including a Single-Layer Perceptron for regression. The output of this Single-Layer Perceptron is used to construct a probability map on the antigen surface. The predictive performances of the method are assessed by computing the AUC using cross validation on two benchmark data sets and by computing the AUC and the precision for a third independent test set.

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Index Terms:
Epitope, antigen, regression, machine learning.
Citation:
Jérôme Ambroise, Joachim Giard, Jean-Luc Gala, Benoit Macq, "Identification of Relevant Properties for Epitopes Detection Using a Regression Model," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1700-1707, Nov.-Dec. 2011, doi:10.1109/TCBB.2011.77
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