Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2164-11-S4-S21
Title: SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
Authors: Wee, L.J.K
Simarmata, D
Kam, Y.-W
Ng, L.F.P 
Tong, J.C 
Keywords: antigen
epitope
glutamine
proline
tryptophan
peptide
access to information
accuracy
amino acid composition
article
B lymphocyte
Bayes theorem
computer model
epitope mapping
Internet
mathematical analysis
prediction
quality control
receiver operating characteristic
sequence analysis
support vector machine
algorithm
chemistry
computer simulation
immunology
predictive value
Benchmark datasets
Data sets
Experimental approaches
High variability
In-silico
Prediction algorithms
Prediction model
SVM classifiers
Test sets
Web servers
Algorithms
Bioinformatics
Classification (of information)
Feature extraction
Forecasting
Mathematical models
Polypeptides
Proteins
Support vector machines
Antigens
Algorithms
Antigens
Bayes Theorem
Benchmarking
Computer Simulation
Epitopes, B-Lymphocyte
Internet
Peptides
Predictive Value of Tests
Issue Date: 2010
Citation: Wee, L.J.K, Simarmata, D, Kam, Y.-W, Ng, L.F.P, Tong, J.C (2010). SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction. BMC Genomics 11 (SUPPL. 4) : S21. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2164-11-S4-S21
Rights: Attribution 4.0 International
Abstract: Backgound: The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply.Results: We analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes.Conclusion: We developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html. © 2010 Wee et al; licensee BioMed Central Ltd.
Source Title: BMC Genomics
URI: https://scholarbank.nus.edu.sg/handle/10635/181652
ISSN: 14712164
DOI: 10.1186/1471-2164-11-S4-S21
Rights: Attribution 4.0 International
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