MHC binding prediction with KernelRLSpan and its variations.

[1]  Ann B. Lee,et al.  Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[3]  Alessandro Sette,et al.  Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method , 2005, BMC Bioinformatics.

[4]  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.

[5]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[6]  Hau-San Wong,et al.  Introduction to the Peptide Binding Problem of Computational Immunology: New Results , 2014, Found. Comput. Math..

[7]  Morten Nielsen,et al.  NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11 , 2008, Nucleic Acids Res..

[8]  W. Bodmer,et al.  Nomenclature for factors of the HLA system, 2010 , 2010, Tissue antigens.

[9]  Morten Nielsen,et al.  Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan , 2008, PLoS Comput. Biol..

[10]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[11]  J. Neefjes,et al.  Towards a systems understanding of MHC class I and MHC class II antigen presentation , 2011, Nature Reviews Immunology.

[12]  Saburou Saitoh,et al.  Theory of Reproducing Kernels and Its Applications , 1988 .

[13]  Hiroshi Mamitsuka,et al.  Towardmore accurate pan-specific MHC-peptide binding prediction : a review of current methods and tools , 2012 .

[14]  Morten Nielsen,et al.  A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules , 2006, PLoS Comput. Biol..

[15]  O. Lund,et al.  NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure , 2010, Immunome research.

[16]  Steven G.E. Marsh,et al.  Nomenclature for factors of the HLA system , 1975 .

[17]  Morten Nielsen,et al.  State of the art and challenges in sequence based T-cell epitope prediction , 2010, Immunome research.

[18]  Ora Schueler-Furman,et al.  Learning MHC I - peptide binding , 2006, ISMB.

[19]  Hsuan-Tien Lin,et al.  A note on Platt’s probabilistic outputs for support vector machines , 2007, Machine Learning.

[20]  Felipe Cucker,et al.  Learning Theory: An Approximation Theory Viewpoint: Index , 2007 .

[21]  S. Smale,et al.  Learning Theory Estimates via Integral Operators and Their Approximations , 2007 .

[22]  I. Svane,et al.  MHC class II-bound self-peptides can be effectively separated by isoelectric focusing and bind optimally to their MHC class II restriction elements around pH 5.0. , 1994, Immunology.

[23]  Bjoern Peters,et al.  Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications , 2005, Immunogenetics.

[24]  Morten Nielsen,et al.  NetMHCcons: a consensus method for the major histocompatibility complex class I predictions , 2011, Immunogenetics.

[25]  Ilka Hoof,et al.  Proteome Sampling by the HLA Class I Antigen Processing Pathway , 2012, PLoS Comput. Biol..

[26]  O. Lund,et al.  The Immune Epitope Database and Analysis Resource: From Vision to Blueprint , 2005, PLoS biology.

[27]  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..

[28]  Rainer Blasczyk,et al.  Nomenclature for factors of the HLA system , 1998 .

[29]  O. Lund,et al.  novel sequence representations Reliable prediction of T-cell epitopes using neural networks with , 2003 .