{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:23:43Z","timestamp":1772771023083,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2006,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as \"binders\" or \"non-binders\" or as \"strong binders\" and \"weak binders\", recent methods seek to make predictions about precise binding affinities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the \"additive method\". By adopting a new \"11-factor encoding\" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-7-182","type":"journal-article","created":{"date-parts":[[2006,4,19]],"date-time":"2006-04-19T12:43:28Z","timestamp":1145450608000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models"],"prefix":"10.1186","volume":"7","author":[{"given":"Wen","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangshan","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darren R","family":"Flower","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongbin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2006,3,31]]},"reference":[{"issue":"1","key":"921_CR1","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1110\/ps.041059505","volume":"14","author":"MJ Blythe","year":"2005","unstructured":"Blythe MJ, Flower DR: Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Sci 2005, 14(1):246\u2013248. 10.1110\/ps.041059505","journal-title":"Protein Sci"},{"key":"921_CR2","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1039\/9781847550705","volume-title":"Drug Design: Cutting Edge Approaches","author":"DR Flower","year":"2002","unstructured":"Flower DR, Doytchinova IA, Paine K, P. T, Blythe MJ, Lamponi D, Zygouri C, Guan P, McSparron H, H. K: Computational Vaccine Design. In Drug Design: Cutting Edge Approaches. Edited by: Flower DR. Cambridge, Royal Society of Chemisty; 2002:136\u2013180."},{"issue":"9","key":"921_CR3","doi-asserted-by":"publisher","first-page":"3296","DOI":"10.1073\/pnas.86.9.3296","volume":"86","author":"A Sette","year":"1989","unstructured":"Sette A, Buus S, Appella E, Smith JA, Chesnut R, Miles C, Colon SM, Grey HM: Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proc Natl Acad Sci U S A 1989, 86(9):3296\u20133300. 10.1073\/pnas.86.9.3296","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"9","key":"921_CR4","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1093\/bioinformatics\/bth100","volume":"20","author":"M Nielsen","year":"2004","unstructured":"Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S, Lund O: Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 2004, 20(9):1388\u20131397. 10.1093\/bioinformatics\/bth100","journal-title":"Bioinformatics"},{"issue":"3-4","key":"921_CR5","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s002510050595","volume":"50","author":"H Rammensee","year":"1999","unstructured":"Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 1999, 50(3\u20134):213\u2013219. 10.1007\/s002510050595","journal-title":"Immunogenetics"},{"issue":"1","key":"921_CR6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.4049\/jimmunol.152.1.163","volume":"152","author":"KC Parker","year":"1994","unstructured":"Parker KC, Bednarek MA, Coligan JE: Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol 1994, 152(1):163\u2013175.","journal-title":"J Immunol"},{"issue":"9","key":"921_CR7","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/S0198-8859(02)00432-9","volume":"63","author":"PA Reche","year":"2002","unstructured":"Reche PA, Glutting JP, Reinherz EL: Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 2002, 63(9):701\u2013709. 10.1016\/S0198-8859(02)00432-9","journal-title":"Hum Immunol"},{"issue":"5","key":"921_CR8","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1110\/ps.0239403","volume":"12","author":"M Nielsen","year":"2003","unstructured":"Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O: Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 2003, 12(5):1007\u20131017. 10.1110\/ps.0239403","journal-title":"Protein Sci"},{"issue":"2","key":"921_CR9","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1093\/bioinformatics\/14.2.121","volume":"14","author":"V Brusic","year":"1998","unstructured":"Brusic V, Rudy G, Honeyman G, Hammer J, Harrison L: Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 1998, 14(2):121\u2013130. 10.1093\/bioinformatics\/14.2.121","journal-title":"Bioinformatics"},{"issue":"10","key":"921_CR10","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1038\/nbt1098-966","volume":"16","author":"MC Honeyman","year":"1998","unstructured":"Honeyman MC, Brusic V, Stone NL, Harrison LC: Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol 1998, 16(10):966\u2013969. 10.1038\/nbt1098-966","journal-title":"Nat Biotechnol"},{"issue":"4","key":"921_CR11","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1002\/(SICI)1097-0134(19981201)33:4<460::AID-PROT2>3.0.CO;2-M","volume":"33","author":"H Mamitsuka","year":"1998","unstructured":"Mamitsuka H: Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 1998, 33(4):460\u2013474. 10.1002\/(SICI)1097-0134(19981201)33:4<460::AID-PROT2>3.0.CO;2-M","journal-title":"Proteins"},{"issue":"1","key":"921_CR12","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/1471-2105-3-25","volume":"3","author":"P Donnes","year":"2002","unstructured":"Donnes P, Elofsson A: Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 2002, 3(1):25. 10.1186\/1471-2105-3-25","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"921_CR13","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1093\/bioinformatics\/btg424","volume":"20","author":"M Bhasin","year":"2004","unstructured":"Bhasin M, Raghava GP: SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence. Bioinformatics 2004, 20(3):421\u2013423. 10.1093\/bioinformatics\/btg424","journal-title":"Bioinformatics"},{"issue":"3","key":"921_CR14","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1021\/pr015513z","volume":"1","author":"IA Doytchinova","year":"2002","unstructured":"Doytchinova IA, Blythe MJ, Flower DR: Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201. J Proteome Res 2002, 1(3):263\u2013272. 10.1021\/pr015513z","journal-title":"J Proteome Res"},{"key":"921_CR15","volume-title":"J Chem Inf Mod (in press)","author":"CK Hattotuwagama","year":"2005","unstructured":"Hattotuwagama CK, Toseland CP, Guan P, Taylor DL, Hemsley SL, Doytchinova IA, Flower DR: Class II Mouse Major Histocompatibility Complex Peptide Binding Affinity: In Silico bioinformatic prediction using robust multivariate statistics. J Chem Inf Mod (in press) 2005."},{"issue":"17","key":"921_CR16","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1093\/bioinformatics\/btg312","volume":"19","author":"IA Doytchinova","year":"2003","unstructured":"Doytchinova IA, Flower DR: Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics 2003, 19(17):2263\u20132270. 10.1093\/bioinformatics\/btg312","journal-title":"Bioinformatics"},{"issue":"22","key":"921_CR17","doi-asserted-by":"publisher","first-page":"3274","DOI":"10.1039\/b409656h","volume":"2","author":"CK Hattotuwagama","year":"2004","unstructured":"Hattotuwagama CK, Guan P, Doytchinova IA, Flower DR: New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity. Org Biomol Chem 2004, 2(22):3274\u20133283. 10.1039\/b409656h","journal-title":"Org Biomol Chem"},{"key":"921_CR18","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini N, Shawe-Taylor J: An introduction to support vector machines and other kernel-based learning methods. Cambridge, UK, Cambridge University Press; 2000."},{"key":"921_CR19","unstructured":"SVRMHC supplementary web site [http:\/\/SVRMHC.umn.edu\/SVRMHC]"},{"issue":"4","key":"921_CR20","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1021\/ci049934n","volume":"44","author":"CX Xue","year":"2004","unstructured":"Xue CX, Zhang RS, Liu HX, Liu MC, Hu ZD, Fan BT: Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity. J Chem Inf Comput Sci 2004, 44(4):1267\u20131274. 10.1021\/ci049934n","journal-title":"J Chem Inf Comput Sci"},{"issue":"4","key":"921_CR21","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1021\/ci049965i","volume":"44","author":"XJ Yao","year":"2004","unstructured":"Yao XJ, Panaye A, Doucet JP, Zhang RS, Chen HF, Liu MC, Hu ZD, Fan BT: Comparative study of QSAR\/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. J Chem Inf Comput Sci 2004, 44(4):1257\u20131266. 10.1021\/ci049965i","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"921_CR22","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/S0893-6080(03)00169-2","volume":"17","author":"V Cherkassky","year":"2004","unstructured":"Cherkassky V, Ma Y: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 2004, 17(1):113\u2013126. 10.1016\/S0893-6080(03)00169-2","journal-title":"Neural Netw"},{"issue":"1","key":"921_CR23","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1021\/ci034173u","volume":"44","author":"HX Liu","year":"2004","unstructured":"Liu HX, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT: Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs. J Chem Inf Comput Sci 2004, 44(1):161\u2013167. 10.1021\/ci034173u","journal-title":"J Chem Inf Comput Sci"},{"issue":"15","key":"921_CR24","doi-asserted-by":"publisher","first-page":"6990","DOI":"10.1158\/0008-5472.CAN-04-3669","volume":"65","author":"Y Huang","year":"2005","unstructured":"Huang Y, Fayad R, Smock A, Ullrich AM, Qiao L: Induction of mucosal and systemic immune responses against human carcinoembryonic antigen by an oral vaccine. Cancer Res 2005, 65(15):6990\u20136999. 10.1158\/0008-5472.CAN-04-3669","journal-title":"Cancer Res"},{"issue":"7","key":"921_CR25","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1128\/IAI.70.7.3336-3343.2002","volume":"70","author":"A Saren","year":"2002","unstructured":"Saren A, Pascolo S, Stevanovic S, Dumrese T, Puolakkainen M, Sarvas M, Rammensee HG, Vuola JM: Identification of Chlamydia pneumoniae-derived mouse CD8 epitopes. Infect Immun 2002, 70(7):3336\u20133343. 10.1128\/IAI.70.7.3336-3343.2002","journal-title":"Infect Immun"},{"issue":"8","key":"921_CR26","doi-asserted-by":"publisher","first-page":"4568","DOI":"10.1128\/JVI.79.8.4568-4579.2005","volume":"79","author":"MC Jaimes","year":"2005","unstructured":"Jaimes MC, Feng N, Greenberg HB: Characterization of homologous and heterologous rotavirus-specific T-cell responses in infant and adult mice. J Virol 2005, 79(8):4568\u20134579. 10.1128\/JVI.79.8.4568-4579.2005","journal-title":"J Virol"},{"issue":"8","key":"921_CR27","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1046\/j.1365-3024.2002.00479.x","volume":"24","author":"RA Wrightsman","year":"2002","unstructured":"Wrightsman RA, Luhrs KA, Fouts D, Manning JE: Paraflagellar rod protein-specific CD8+ cytotoxic T lymphocytes target Trypanosoma cruzi-infected host cells. Parasite Immunol 2002, 24(8):401\u2013412. 10.1046\/j.1365-3024.2002.00479.x","journal-title":"Parasite Immunol"},{"issue":"16","key":"921_CR28","doi-asserted-by":"publisher","first-page":"8468","DOI":"10.1128\/JVI.78.16.8468-8476.2004","volume":"78","author":"S Peng","year":"2004","unstructured":"Peng S, Ji H, Trimble C, He L, Tsai YC, Yeatermeyer J, Boyd DA, Hung CF, Wu TC: Development of a DNA vaccine targeting human papillomavirus type 16 oncoprotein E6. J Virol 2004, 78(16):8468\u20138476. 10.1128\/JVI.78.16.8468-8476.2004","journal-title":"J Virol"},{"issue":"1","key":"921_CR29","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.virol.2005.01.050","volume":"335","author":"Y Zhi","year":"2005","unstructured":"Zhi Y, Kobinger GP, Jordan H, Suchma K, Weiss SR, Shen H, Schumer G, Gao G, Boyer JL, Crystal RG, Wilson JM: Identification of murine CD8 T cell epitopes in codon-optimized SARS-associated coronavirus spike protein. Virology 2005, 335(1):34\u201345. 10.1016\/j.virol.2005.01.050","journal-title":"Virology"},{"issue":"9","key":"921_CR30","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.1110\/ps.9.9.1838","volume":"9","author":"O Schueler-Furman","year":"2000","unstructured":"Schueler-Furman O, Altuvia Y, Sette A, Margalit H: Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci 2000, 9(9):1838\u20131846.","journal-title":"Protein Sci"},{"issue":"11","key":"921_CR31","doi-asserted-by":"publisher","first-page":"6813","DOI":"10.4049\/jimmunol.173.11.6813","volume":"173","author":"I Doytchinova","year":"2004","unstructured":"Doytchinova I, Hemsley S, Flower DR: Transporter associated with antigen processing preselection of peptides binding to the MHC: a bioinformatic evaluation. J Immunol 2004, 173(11):6813\u20136819.","journal-title":"J Immunol"},{"issue":"5","key":"921_CR32","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1093\/bioinformatics\/btg055","volume":"19","author":"M Bhasin","year":"2003","unstructured":"Bhasin M, Singh H, Raghava GP: MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 2003, 19(5):665\u2013666. 10.1093\/bioinformatics\/btg055","journal-title":"Bioinformatics"},{"issue":"22","key":"921_CR33","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.1021\/jm010021j","volume":"44","author":"IA Doytchinova","year":"2001","unstructured":"Doytchinova IA, Flower DR: Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. J Med Chem 2001, 44(22):3572\u20133581. 10.1021\/jm010021j","journal-title":"J Med Chem"},{"issue":"3","key":"921_CR34","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1002\/prot.10154","volume":"48","author":"IA Doytchinova","year":"2002","unstructured":"Doytchinova IA, Flower DR: Physicochemical explanation of peptide binding to HLA-A*0201 major histocompatibility complex: a three-dimensional quantitative structure-activity relationship study. Proteins 2002, 48(3):505\u2013518. 10.1002\/prot.10154","journal-title":"Proteins"},{"key":"921_CR35","volume-title":"Statistical Learning Theory","author":"V Vapnik","year":"1998","unstructured":"Vapnik V: Statistical Learning Theory. New York, John Wiley & Sons; 1998."},{"issue":"15","key":"921_CR36","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.1093\/bioinformatics\/btg255","volume":"19","author":"Y Zhao","year":"2003","unstructured":"Zhao Y, Pinilla C, Valmori D, Martin R, Simon R: Application of support vector machines for T-cell epitopes prediction. Bioinformatics 2003, 19(15):1978\u20131984. 10.1093\/bioinformatics\/btg255","journal-title":"Bioinformatics"},{"issue":"13","key":"921_CR37","doi-asserted-by":"publisher","first-page":"3621","DOI":"10.1093\/nar\/gkg510","volume":"31","author":"P Guan","year":"2003","unstructured":"Guan P, Doytchinova IA, Zygouri C, Flower DR: MHCPred: A server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res 2003, 31(13):3621\u20133624. 10.1093\/nar\/gkg510","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"921_CR38","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/1745-7580-1-4","volume":"1","author":"CP Toseland","year":"2005","unstructured":"Toseland CP, Clayton DJ, McSparron H, Hemsley SL, Blythe MJ, Paine K, Doytchinova IA, Guan P, Hattotuwagama CK, Flower DR: AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res 2005, 1(1):4. 10.1186\/1745-7580-1-4","journal-title":"Immunome Res"},{"key":"921_CR39","volume-title":"A practical guide to SVM classification, LibSVM documentation","author":"CC Chang","year":"2004","unstructured":"Chang CC, Lin CJ: A practical guide to SVM classification, LibSVM documentation. 2004."},{"key":"921_CR40","volume-title":"Bioinformatics: the machine learning approach","author":"P Baldi","year":"2001","unstructured":"Baldi P, Brunak S: Bioinformatics: the machine learning approach. Cambridge, MA, The MIT Press; 2001."},{"issue":"1","key":"921_CR41","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1093\/nar\/27.1.368","volume":"27","author":"S Kawashima","year":"1999","unstructured":"Kawashima S, Ogata H, Kanehisa M: AAindex: Amino Acid Index Database. Nucleic Acids Res 1999, 27(1):368\u2013369. 10.1093\/nar\/27.1.368","journal-title":"Nucleic Acids Res"},{"key":"921_CR42","unstructured":"MHCPred [http:\/\/www.jenner.ac.uk\/MHCPred\/]"},{"key":"921_CR43","unstructured":"SYFPEITHI [http:\/\/www.syfpeithi.de\/Scripts\/MHCServer.dll\/EpitopePrediction.htm]"},{"key":"921_CR44","unstructured":"BIMAS [http:\/\/thr.cit.nih.gov\/molbio\/hla_bind\/]"},{"key":"921_CR45","unstructured":"RANKPEP [http:\/\/www.mifoundation.org\/Tools\/rankpep.html]"},{"key":"921_CR46","unstructured":"SVMHC [http:\/\/www-bs.informatik.uni-tuebingen.de\/SVMHC]"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-7-182.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T03:51:35Z","timestamp":1683431495000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-7-182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,3,31]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2006,12]]}},"alternative-id":["921"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-7-182","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,3,31]]},"assertion":[{"value":"22 December 2005","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2006","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2006","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"182"}}