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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 |
Appears in Collections: | Elements Staff Publications |
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