Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network

By Sudipto Saha, Gajendra P. Raghava. Proteins: Structure, Function, and Bioinformatics, 2006

1,598 citations recorded by the source corpus.

Subfields: Immunology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4c948729bac30e754c51a92a348cf4c5e499cbce

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Citations in the corpus

High-throughput prediction of protein antigenicity using protein microarray data (2010, background): “…1990; Thornton et al., 1986; Welling et al., 1985) and is still an accepted approach for modern vaccinology (Andersen et al., 2006; Larsen et al., 2006; Odorico and Pellequer, 2003; Rubinstein et al., 2009; Saha and Raghava, 2006; Söllner and Mayer, 2006; Sweredoski and Baldi, 2008, 2009).”
Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “ABCpred and LBtope methods consist on artificial neural networks (ANNs) trained on similar positive data, B-cell epitopes, but differ on negative data, non-Bcell epitopes.”
Predicting linear B-cell epitopes using string kernels (2008, neutral): “In our experiments using the Saha and Raghava (2006b) 16-mer peptide data set (containing approximately 700 B-cell epitopes and 700 non-epitope peptides) on which ABCPred had the best reported performance of 66%, both BCPred and AAP outperformed ABCPred, based on fivefold cross-validation.”
In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology (2022, methodology): “ABCpred server (https:// webs. iiitd. edu. in/ ragha va/ abcpr ed/) was used to predict B cell epitopes (Saha and Raghava 2006).”
Analysis and prediction of antibacterial peptides (2007, result): “This problem is similar to B-cell epitope prediction [ 27 ].”
VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology (2017, background): “[40] Propred-I Prediction of promiscuous major histocompatibility complex (MHC) Class-I binding sites.”
SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity (2012, methodology): “In addition, based on the epitope databases such as IEDB [9], Bcipep [10], and FIMM [11], there are also some methods using machine learning approaches, such as Hidden Markov Model (HMM) [12], Artificial Neural Network (ANN) [13], and Support Vector Machine (SVM) [14,15], to locate linear epitopes, ”