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|Title: ||Linear epitope prediction with a combination of support vector machine classification and amino acid propensity identification|
|Authors: ||Hsin-Wei Wang;Ya-Chi Lin;Tun-Wen Pai;Hao-Teng Chang|
|Contributors: ||NTOU:Department of Computer Science and Engineering|
|Keywords: ||Linear epitope;antigenicity;support vector machine;machine learning;immunology|
|Issue Date: ||2011-10-21T02:35:02Z
|Publisher: ||Journal of Biomedicine and Biotechnology|
|Abstract: ||Abstract:Epitopes are antigenic determinants that are useful because they induce B cell antibody production and stimulate T cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physico-chemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews correlation coefficient (10.36%). The LEPS is freely available for academic use at http://LEPS.cs.ntou.edu.tw.|
|Appears in Collections:||[資訊工程學系] 期刊論文|
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