SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

10.1186/1471-2164-11-S4-S21

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Main Authors:	Wee, L.J.K, Simarmata, D, Kam, Y.-W, Ng, L.F.P, Tong, J.C
Other Authors:	BIOCHEMISTRY
Format:	Article
Published:	2020
Subjects:	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 Bayes Theorem Benchmarking Computer Simulation Epitopes, B-Lymphocyte Peptides Predictive Value of Tests
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/181652
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Institution:	National University of Singapore

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spelling	sg-nus-scholar.10635-1816522024-11-08T19:19:27Z SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction Wee, L.J.K Simarmata, D Kam, Y.-W Ng, L.F.P Tong, J.C BIOCHEMISTRY 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 10.1186/1471-2164-11-S4-S21 BMC Genomics 11 SUPPL. 4 S21 2020-10-27T11:35:56Z 2020-10-27T11:35:56Z 2010 Article 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 14712164 https://scholarbank.nus.edu.sg/handle/10635/181652 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ Unpaywall 20201031
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	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
spellingShingle	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 Wee, L.J.K Simarmata, D Kam, Y.-W Ng, L.F.P Tong, J.C SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
description	10.1186/1471-2164-11-S4-S21
author2	BIOCHEMISTRY
author_facet	BIOCHEMISTRY Wee, L.J.K Simarmata, D Kam, Y.-W Ng, L.F.P Tong, J.C
format	Article
author	Wee, L.J.K Simarmata, D Kam, Y.-W Ng, L.F.P Tong, J.C
author_sort	Wee, L.J.K
title	SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
title_short	SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
title_full	SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
title_fullStr	SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
title_full_unstemmed	SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
title_sort	svm-based prediction of linear b-cell epitopes using bayes feature extraction
publishDate	2020
url	https://scholarbank.nus.edu.sg/handle/10635/181652
_version_	1821220137430155264

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

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