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Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification
Joint Authors
Chang, Hao-Teng
Pai, Tun-Wen
Wang, Hsin-Wei
Lin, Ya-Chi
Source
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-08-23
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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 B-cell 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 physicochemical 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%).
American Psychological Association (APA)
Wang, Hsin-Wei& Lin, Ya-Chi& Pai, Tun-Wen& Chang, Hao-Teng. 2011. Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification. BioMed Research International،Vol. 2011, no. 2011, pp.1-12.
https://search.emarefa.net/detail/BIM-990093
Modern Language Association (MLA)
Wang, Hsin-Wei…[et al.]. Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification. BioMed Research International No. 2011 (2011), pp.1-12.
https://search.emarefa.net/detail/BIM-990093
American Medical Association (AMA)
Wang, Hsin-Wei& Lin, Ya-Chi& Pai, Tun-Wen& Chang, Hao-Teng. Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification. BioMed Research International. 2011. Vol. 2011, no. 2011, pp.1-12.
https://search.emarefa.net/detail/BIM-990093
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-990093