Extraction of Immune Epitope Information
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[1] Morten Nielsen,et al. An automated benchmarking platform for MHC class II binding prediction methods , 2018, Bioinform..
[2] Purvesh Khatri,et al. Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes , 2017, Cell.
[3] M. Nielsen,et al. NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data , 2017, The Journal of Immunology.
[4] C. Melief. Cancer: Precision T-cell therapy targets tumours , 2017, Nature.
[5] J. Utikal,et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer , 2017, Nature.
[6] Charles H. Yoon,et al. An immunogenic personal neoantigen vaccine for patients with melanoma , 2017, Nature.
[7] Bjoern Peters,et al. The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design , 2017, Front. Immunol..
[8] Vladimir Brusic,et al. TANTIGEN: a comprehensive database of tumor T cell antigens , 2017, Cancer Immunology, Immunotherapy.
[9] Jennifer G. Abelin,et al. Mass Spectrometry Profiling of HLA‐Associated Peptidomes in Mono‐allelic Cells Enables More Accurate Epitope Prediction , 2017, Immunity.
[10] D. Barouch,et al. New concepts in HIV-1 vaccine development. , 2016, Current opinion in immunology.
[11] R. Rappuoli,et al. Reverse vaccinology 2.0: Human immunology instructs vaccine antigen design , 2016, The Journal of experimental medicine.
[12] Morten Nielsen,et al. Gapped sequence alignment using artificial neural networks: application to the MHC class I system , 2016, Bioinform..
[13] V. Brusic,et al. FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses , 2015, Journal of immunology research.
[14] Morten Nielsen,et al. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification , 2015, Immunogenetics.
[15] D. Keskin,et al. EBVdb: a data mining system for knowledge discovery in Epstein-Barr virus with applications in T cell immunology and vaccinology , 2015, 2015 International Workshop on Artificial Immune Systems (AIS).
[16] Morten Nielsen,et al. Automated benchmarking of peptide-MHC class I binding predictions , 2015, Bioinform..
[17] Guang Lan Zhang,et al. Physical detection of influenza A epitopes identifies a stealth subset on human lung epithelium evading natural CD8 immunity , 2015, Proceedings of the National Academy of Sciences.
[18] James Robinson,et al. The IPD and IMGT/HLA database: allele variant databases , 2014, Nucleic Acids Res..
[19] Deborah Hix,et al. The immune epitope database (IEDB) 3.0 , 2014, Nucleic Acids Res..
[20] Richard A. Olshen,et al. Diversity and clonal selection in the human T-cell repertoire , 2014, Proceedings of the National Academy of Sciences.
[21] B. Korber,et al. Characterization and Immunogenicity of a Novel Mosaic M HIV-1 gp140 Trimer , 2014, Journal of Virology.
[22] Vladimir Brusic,et al. Big Data Analytics in Immunology: A Knowledge-Based Approach , 2014, BioMed research international.
[23] Vladimir Brusic,et al. HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology , 2013, BCB.
[24] O. Lund,et al. NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ , 2013, Immunogenetics.
[25] N. Hacohen,et al. Cancer Immunology at the Crossroads : Functional Genomics Getting Personal with Neoantigen-Based Therapeutic Cancer Vaccines , 2013 .
[26] Nathalie Vigneron,et al. Database of T cell-defined human tumor antigens: the 2013 update. , 2013, Cancer immunity.
[27] Hau-San Wong,et al. TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules , 2012, PloS one.
[28] V. Brusic,et al. FLAVIdB: A data mining system for knowledge discovery in flaviviruses with direct applications in immunology and vaccinology , 2013, Immunome research.
[29] Morten Nielsen,et al. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions , 2011, Immunogenetics.
[30] Vladimir Brusic,et al. Dana-Farber repository for machine learning in immunology. , 2011, Journal of immunological methods.
[31] Harris Papadopoulos,et al. Machine learning competition in immunology - Prediction of HLA class I binding peptides. , 2011, Journal of immunological methods.
[32] R. Berkowitz,et al. Direct Identification of an HPV-16 Tumor Antigen from Cervical Cancer Biopsy Specimens , 2011, Front. Immun..
[33] Jörn Dengjel,et al. Mass spectrometry analysis and quantitation of peptides presented on the MHC II molecules of mouse spleen dendritic cells. , 2011, Journal of proteome research.
[34] O. Lund,et al. MULTIPRED2: A computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles , 2010, Journal of Immunological Methods.
[35] D. Keskin,et al. A Conserved E7-derived Cytotoxic T Lymphocyte Epitope Expressed on Human Papillomavirus 16-transformed HLA-A2+ Epithelial Cancers , 2010, The Journal of Biological Chemistry.
[36] Uthaman Gowthaman,et al. Evaluation of different generic in silico methods for predicting HLA class I binding peptide vaccine candidates using a reverse approach , 2010, Amino Acids.
[37] Morten Nielsen,et al. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction , 2009, BMC Bioinformatics.
[38] Morten Nielsen,et al. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding , 2009, Bioinform..
[39] M. V. Regenmortel,et al. What is a B-cell epitope? , 2009 .
[40] Sneh Lata,et al. MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes , 2009, BMC Research Notes.
[41] Vladimir Brusic,et al. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research , 2008, BMC Bioinformatics.
[42] John Sidney,et al. A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach , 2008, PLoS Comput. Biol..
[43] O. Lund,et al. NetMHCpan, a method for MHC class I binding prediction beyond humans , 2008, Immunogenetics.
[44] V. Brusic,et al. Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research , 2008, BMC Immunology.
[45] O. Lund,et al. NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence , 2007, PloS one.
[46] Morten Nielsen,et al. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method , 2007, BMC Bioinformatics.
[47] Mathias M Schuler,et al. SYFPEITHI: database for searching and T-cell epitope prediction. , 2007, Methods in molecular biology.
[48] Oliver Kohlbacher,et al. SVMHC: a server for prediction of MHC-binding peptides , 2006, Nucleic Acids Res..
[49] Morten Nielsen,et al. A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules , 2006, PLoS Comput. Biol..
[50] P. van Endert,et al. Complexity, contradictions, and conundrums: studying post‐proteasomal proteolysis in HLA class I antigen presentation , 2005, Immunological reviews.
[51] Elke Krüger,et al. Interferon‐γ, the functional plasticity of the ubiquitin–proteasome system, and MHC class I antigen processing , 2005, Immunological reviews.
[52] P. Dönnes,et al. Integrated modeling of the major events in the MHC class I antigen processing pathway , 2005, Protein science : a publication of the Protein Society.
[53] Gajendra P. S. Raghava,et al. Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences , 2005, Nucleic Acids Res..
[54] Alessandro Sette,et al. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method , 2005, BMC Bioinformatics.
[55] O. Lund,et al. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage , 2005, Immunogenetics.
[56] Karina Yusim,et al. Los Alamos Hepatitis C Immunology Database , 2005, Applied bioinformatics.
[57] Ellis L. Reinherz,et al. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles , 2004, Immunogenetics.
[58] A. Purcell,et al. Immunoproteomics , 2004, Molecular & Cellular Proteomics.
[59] Manoj Bhasin,et al. Analysis and prediction of affinity of TAP binding peptides using cascade SVM , 2004, Protein science : a publication of the Protein Society.
[60] S. Brunak,et al. Predicting proteasomal cleavage sites: a comparison of available methods. , 2003, International immunology.
[61] Gajendra P. S. Raghava,et al. ProPred1: Prediction of Promiscuous MHC Class-I Binding Sites , 2003, Bioinform..
[62] O. Lund,et al. novel sequence representations Reliable prediction of T-cell epitopes using neural networks with , 2003 .
[63] P. Kloetzel,et al. MAPPP: MHC class I antigenic peptide processing prediction. , 2003, Applied bioinformatics.
[64] Rino Rappuoli,et al. Reverse vaccinology. , 2000, Current opinion in microbiology.
[65] E. Reinherz,et al. Prediction of MHC class I binding peptides using profile motifs. , 2002, Human immunology.
[66] Vladimir Brusic,et al. Prediction of promiscuous peptides that bind HLA class I molecules , 2002, Immunology and cell biology.
[67] Jia-huai Wang,et al. Structural basis of T cell recognition of peptides bound to MHC molecules. , 2002, Molecular immunology.
[68] D. Keskin,et al. Cells Expressing Indoleamine 2,3-Dioxygenase Inhibit T Cell Responses1 , 2002, The Journal of Immunology.
[69] S. Brunak,et al. Prediction of proteasome cleavage motifs by neural networks. , 2002, Protein engineering.
[70] Gajendra P. S. Raghava,et al. ProPred: prediction of HLA-DR binding sites , 2001, Bioinform..
[71] K. Hadeler,et al. PAProC: a prediction algorithm for proteasomal cleavages available on the WWW , 2001, Immunogenetics.
[72] V. Jongeneel. Towards a cancer immunome database. , 2001, Cancer immunity.
[73] R. Rappuoli,et al. Reverse vaccinology. , 2000, Current opinion in microbiology.
[74] J. Sidney,et al. Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism , 1999, Immunogenetics.
[75] H. Rammensee,et al. SYFPEITHI: database for MHC ligands and peptide motifs , 1999, Immunogenetics.
[76] U. Şahin,et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices , 1999, Nature Biotechnology.
[77] P. Kloetzel,et al. A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome. , 1999, Journal of molecular biology.
[78] Vladimir Brusic,et al. A neural network model approach to the study of human TAP transporter , 1998, Silico Biol..
[79] A Sette,et al. Practical, biochemical and evolutionary implications of the discovery of HLA class I supermotifs. , 1996, Immunology today.
[80] L C Harrison,et al. MHCPEP: a database of MHC-binding peptides. , 1994, Nucleic acids research.
[81] Don C. Wiley,et al. Crystal structure of the human class II MHC protein HLA-DR1 complexed with an influenza virus peptide , 1994, Nature.
[82] K. Parker,et al. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. , 1994, Journal of immunology.
[83] V. Engelhard,et al. Structure of peptides associated with class I and class II MHC molecules. , 1994, Annual review of immunology.
[84] A. Rudensky,et al. Sequence analysis of peptides bound to MHC class II molecules , 1991, Nature.
[85] M. Egerton,et al. The generation and fate of thymocytes. , 1990, Seminars in immunology.
[86] Mark M. Davis,et al. T-cell antigen receptor genes and T-cell recognition , 1988, Nature.
[87] E. Reinherz,et al. Clonal analysis of human cytotoxic T lymphocytes: T4+ and T8+ effector T cells recognize products of different major histocompatibility complex regions. , 1982, Proceedings of the National Academy of Sciences of the United States of America.