DynaPred: A structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations, Bioinformatics, vol.22, issue.14, pp.22-38, 2006. ,
DOI : 10.1093/bioinformatics/btl216
Theory of reproducing kernels, Transactions of the American Mathematical Society, vol.68, issue.3, pp.337-404, 1950. ,
DOI : 10.1090/S0002-9947-1950-0051437-7
URL : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA296533
Prediction of CTL epitopes using QM, SVM and ANN techniques, Vaccine, vol.22, issue.23-24, pp.23-24, 2004. ,
DOI : 10.1016/j.vaccine.2004.02.005
URL : http://repository.ias.ac.in/37239/1/37239.pdf
MHCBN: a comprehensive database of MHC binding and non-binding peptides, Bioinformatics, vol.19, issue.5, pp.665-666, 2003. ,
DOI : 10.1093/bioinformatics/btg055
Prediction of promiscuous peptides that bind HLA class I molecules, Immunology and Cell Biology, vol.107, issue.3, pp.280-285, 2002. ,
DOI : 10.1002/1521-4141(200107)31:7<1989::AID-IMMU1989>3.0.CO;2-M
Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications, Immunogenetics, vol.51, issue.5, pp.304-314, 2005. ,
DOI : 10.1007/s00251-005-0798-y
Structural prediction of peptides binding to MHC class I molecules, Proteins: Structure, Function, and Bioinformatics, vol.13, issue.51, pp.43-52, 2006. ,
DOI : 10.1002/prot.20870
Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach, Tissue Antigens, vol.62, issue.5, pp.62-378, 2003. ,
DOI : 10.1034/j.1399-0039.2003.00112.x
Prediction of MHC class I binding peptides, using SVMHC, BMC Bioinformatics, vol.3, issue.1, p.25, 2002. ,
DOI : 10.1186/1471-2105-3-25
Identifiying Human MHC Supertypes Using Bioinformatic Methods, The Journal of Immunology, vol.172, issue.7, pp.4314-4323, 2004. ,
DOI : 10.4049/jimmunol.172.7.4314
Learning multiple tasks with kernel methods, J. Mach. Learn. Res, vol.6, pp.615-637, 2005. ,
Leveraging information across HLA alleles/supertypes improves HLA-specific epitope prediction, 2006. ,
DOI : 10.1089/cmb.2007.r013
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.164.715
PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions, BMC Bioinformatics, vol.7, issue.Suppl 1, 2006. ,
DOI : 10.1186/1471-2105-7-S1-S3
Identifying HLA supertypes by learning distance functions, Bioinformatics, vol.23, issue.2, pp.148-155, 2007. ,
DOI : 10.1093/Bioinformatics/btl324
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.578.4719
Neural networkbased prediction of candidate T-cell epitopes, Nat. Biotechnol, issue.10, pp.16-966, 1998. ,
Learning MHC I--peptide binding, Bioinformatics, vol.22, issue.14, pp.22-227, 2006. ,
DOI : 10.1093/bioinformatics/btl255
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.332.9952
Predicting peptides that bind to MHC molecules using supervised learning of hidden markov models, Proteins: Structure, Function, and Genetics, vol.213, issue.4, pp.460-474, 1998. ,
DOI : 10.1002/(SICI)1097-0134(19981201)33:4<460::AID-PROT2>3.0.CO;2-M
OPINION ??? VACCINES: The quest for an AIDS vaccine: is the CD8+ T-cell approach feasible?, Nature Reviews Immunology, vol.71, issue.4, pp.283-291, 2002. ,
DOI : 10.1038/nm1201-1320
Application of an artificial neural network to predict specific class I MHC binding peptide sequences, Nature Biotechnology, vol.20, issue.8, pp.16-753, 1998. ,
DOI : 10.1016/0042-6822(88)90065-7
Reliable prediction of T-cell epitopes using neural networks with novel sequence representations, Protein Science, vol.13, issue.5, pp.1007-1017, 2003. ,
DOI : 10.1110/ps.0239403
Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide sidechains, J. Immunol, vol.152, issue.1, pp.163-175, 1994. ,
Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method, BMC Bioinformatics, vol.6, issue.1, p.132, 2005. ,
DOI : 10.1186/1471-2105-6-132
A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules, PLoS Computational Biology, vol.240, issue.6, p.65, 2006. ,
DOI : 10.1371/journal.pcbi.0020065.st001
SYFPEITHI: database for MHC ligands and peptide motifs, Immunogenetics, vol.50, issue.3-4, pp.3-4, 1999. ,
DOI : 10.1007/s002510050595
MHC ligands and peptide motifs: first listing, Immunogenetics, vol.89, issue.4, pp.178-228, 1995. ,
DOI : 10.1007/BF00172063
Prediction of MHC class I binding peptides using profile motifs, Human Immunology, vol.63, issue.9, pp.701-709, 2002. ,
DOI : 10.1016/S0198-8859(02)00432-9
Flexible docking of peptides to class I major-histocompatibility-complex receptors, Genetic Analysis: Biomolecular Engineering, vol.12, issue.1, pp.1-21, 1995. ,
DOI : 10.1016/1050-3862(95)00107-7
Peptide motifs of closely related HLA class I molecules encompass substantial differences, European Journal of Immunology, vol.219, issue.9, pp.2453-2456, 1992. ,
DOI : 10.1002/eji.1830220940
Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores, BMC Bioinformatics, vol.7, issue.1, p.501, 2006. ,
DOI : 10.1186/1471-2105-7-501
Learning with Kernels: Support Vector Machines , Regularization, Optimization, and Beyond, 2002. ,
Kernel Methods in Computational Biology, 2004. ,
Structure-based prediction of binding peptides to MHC class I molecules: Application to a broad range of MHC alleles, Protein Science, vol.189, issue.9, pp.1838-1846, 2000. ,
DOI : 10.1110/ps.9.9.1838
HLA supertypes and supermotifs: a functional perspective on HLA polymorphism, Current Opinion in Immunology, vol.10, issue.4, pp.478-482, 1998. ,
DOI : 10.1016/S0952-7915(98)80124-6
Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism, Immunogenetics, vol.50, issue.3-4, pp.50-53, 1999. ,
DOI : 10.1007/s002510050594
HLA expression in cancer: implications for T cell-based immunotherapy, Immunogenetics, vol.53, issue.4, pp.255-263, 2001. ,
DOI : 10.1007/s002510100334
Definition of an HLA-A3-like supermotif demonstrates the overlapping peptide-binding repertoires of common HLA molecules, Human Immunology, vol.45, issue.2, pp.45-79, 1996. ,
DOI : 10.1016/0198-8859(95)00173-5
Prediction of HLA-DQ3.2beta ligands: evidence of multiple registers in class II binding peptides, Bioinformatics, issue.10, pp.22-1232, 2006. ,
Statistical Learning Theory, 1998. ,
Human tumor antigens: implications for cancer vaccine development, Journal of Molecular Medicine, vol.77, issue.9, pp.640-655, 1999. ,
DOI : 10.1007/s001099900042
IMMUNODOMINANCE IN MAJOR HISTOCOMPATIBILITY COMPLEX CLASS I???RESTRICTED T LYMPHOCYTE RESPONSES, Annual Review of Immunology, vol.17, issue.1, pp.51-88, 1999. ,
DOI : 10.1146/annurev.immunol.17.1.51
MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides, Nucleic Acids Research, vol.33, issue.Web Server, pp.33-172, 2005. ,
DOI : 10.1093/nar/gki452
Application of support vector machines for T-cell epitopes prediction, Bioinformatics, vol.19, issue.15, pp.19-1978, 2003. ,
DOI : 10.1093/bioinformatics/btg255
Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules, Bioinformatics, vol.22, issue.13, pp.22-1648, 2006. ,
DOI : 10.1093/bioinformatics/btl141