SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence - Dalkas, Georgios A.; Rooman, Marianne (BMC bioinformatics, 2017)
Keywords: Algorithms; Amino Acid Sequence; Amino acids; Antigen-Antibody Complex; Area Under Curve; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Epitopes, B-Lymphocyte - chemistry; Epitopes, B-Lymphocyte - immunology; Epitopes, B-Lymphocyte - metabolism; Humans; Immune response; Immunology; Life Sciences; Microarrays; Molecular Sequence Data; Protein Structure, Secondary; Receptors, Adrenergic, beta-2 - chemistry; Receptors, Adrenergic, beta-2 - metabolism; Research Article; ROC Curve; Sequence analysis (applications); B-cell epitopes; β2 adrenergic G-protein-coupled receptor; Machine learning; Statistical potentials; Bioinformatics predictor; Physicochemical properties; Immunoinformatics; Antigen-antibody complexes