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Immunome Research|July 28, 2017
A computational pipeline to generate MHC binding motifsPeng Wang, John Sidney, Alessandro Sette, et al.Seminars in Immunology|November 2, 2020
Epitope prediction and identification- adaptive T cell responses in humansJohn Sidney, Bjoern Peters, Alessandro SettePlos Computational Biology|April 5, 2008
A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approachPeng Wang, John Sidney, Courtney Dow, et al.Current Protocols in Immunology|August 2, 2016
TepiTool: A Pipeline for Computational Prediction of T Cell Epitope CandidatesSinu Paul, John Sidney, Alessandro Sette, et al.BMC Bioinformatics|November 25, 2010
Peptide binding predictions for HLA DR, DP and DQ moleculesPeng Wang, John Sidney, Yohan Kim, et al.BMC Bioinformatics|December 2, 2009
Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian priorYohan Kim, John Sidney, Clemencia Pinilla, et al.BMC Immunology|January 24, 2008
HLA class I supertypes: a revised and updated classificationJohn Sidney, Bjoern Peters, Nicole Frahm, et al.Journal of Biomedicine & Biotechnology|May 22, 2010
IMMUNOCAT-a data management system for epitope mapping studiesJo L Chung, Jian Sun, John Sidney, et al.Immunology|October 16, 2020
Comparison of HLA ligand elution data and binding predictions reveals varying prediction performance for the multiple motifs recognized by HLA-DQ2.5Zeynep Koşaloğlu-Yalçın, John Sidney, William Chronister, et al.BMC Bioinformatics|July 15, 2014
Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictionsYohan Kim, John Sidney, Søren Buus, et al.Pageof 1,262