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

• 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
• Institution: National University of Singapore

10.1186/1471-2164-11-S4-S21