Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes

A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machi...

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Veröffentlicht in:	PLoS computational biology 2018-11, Vol.14 (11), p.e1006457-e1006457
Hauptverfasser:	Zhao, Weilong, Sher, Xinwei
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Affinity Algorithms Alleles Antigenic determinants Antigens Artificial intelligence Artificial neural networks Benchmarking Benchmarks Best practice Binders Binding Bioinformatics Biology and Life Sciences Cancer Cancer Vaccines - immunology Computer and Information Sciences Datasets Datasets as Topic Elution Epitopes Epitopes, T-Lymphocyte - chemistry Epitopes, T-Lymphocyte - immunology Epitopes, T-Lymphocyte - metabolism Histocompatibility antigen HLA Histocompatibility Antigens Class I - immunology Histocompatibility Antigens Class I - metabolism Histocompatibility Antigens Class II - immunology Histocompatibility Antigens Class II - metabolism HLA antigens Humans Immune response Immunogenicity Immunogenicity, Vaccine Immunotherapy Learning algorithms Learning theory Leukocytes Ligands Lymphocytes Lymphocytes T Machine Learning Major histocompatibility complex Major Histocompatibility Complex - immunology Mass spectrometry Medicine and Health Sciences Neural networks Observations Peptides Peptides - chemistry Peptides - immunology Peptides - metabolism Physical Sciences Physiological aspects Predictions Protein Binding Reliability analysis Reproducibility of Results Research and Analysis Methods Scientific imaging T cell receptors T cells T-Lymphocytes - immunology Vaccines
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