Journal of Biomedicine and Biotechnology
Volume 2010 (2010), Article ID 218590, 26 pages
doi:10.1155/2010/218590
Review Article

Emerging Vaccine Informatics

Yongqun He,1 Rino Rappuoli,2 Anne S. De Groot,3,4 and Robert T. Chen5

1Department of Microbiology and Immunology, Unit for Laboratory Animal Medicine, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
2Novartis Vaccines and Diagnostics, 53100 Siena, Italy
3EpiVax, Inc., Providence, RI 02903, USA
4Institute for Immunology and Informatics, University of Rhode Island, Providence, RI 02903, USA
5HIV Vaccine and Special Studies Team, Centers for Disease Control and Prevention (CDC/DHAP/EB), Atlanta, GA 30333, USA

Received 8 December 2010; Accepted 31 December 2010

Academic Editor: Rodomiro Ortiz

Copyright © 2010 Yongqun He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Jenner, An Inquiry into the Causes and Effects of the Variolae Vaccinae, Low, London, UK, 1798.
  2. M. R. Hilleman, “Vaccines in historic evolution and perspective: a narrative of vaccine discoveries,” Vaccine, vol. 18, no. 15, pp. 1436–1447, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Parrino and B. S. Graham, “Smallpox vaccines: past, present, and future,” Journal of Allergy and Clinical Immunology, vol. 118, no. 6, pp. 1320–1326, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  4. E. N. T. Meeusen, J. Walker, A. Peters, P. P. Pastoret, and G. Jungersen, “Current status of veterinary vaccines,” Clinical Microbiology Reviews, vol. 20, no. 3, pp. 489–510, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  5. A. S. De Groot, H. Sbai, C. S. Aubin, J. McMurry, and W. Martin, “Immuno-informatics: mining genomes for vaccine components,” Immunology and Cell Biology, vol. 80, no. 3, pp. 255–269, 2002. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  6. B. Peters, J. Sidney, P. Bourne et al., “The immune epitope database and analysis resource: from vision to blueprint,” PLoS Biology, vol. 3, no. 3, p. e91, 2005. View at Scopus
  7. R. D. Fleischmann, M. D. Adams, O. White et al., “Whose-genome random sequencing and assembly of Haemophilus influenzae Rd,” Science, vol. 269, no. 5223, pp. 496–521, 1995. View at Scopus
  8. R. Rappuoli, “Reverse vaccinology,” Current Opinion in Microbiology, vol. 3, no. 5, pp. 445–450, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. A.S. De Groot, J. McMurry, L. Moise, and B. Martin, “Epitope-based Immunome-derived vaccines: a strategy for improved design and safety,” in Applications of Immunomics, A. Falus, Ed., Springer Immunomics Series, Springer, New York, NY, USA, 2009.
  10. M. Pizza, V. Scarlato, V. Masignani, et al., “Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing,” Science, vol. 287, pp. 1816–1820, 2000.
  11. W. Zhou, V. Pool, J. K. Iskander et al., “Surveillance for safety after immunization: Vaccine Adverse Event Reporting System (VAERS)—United States, 1991–2001,” MMWR. Surveillance Summaries, vol. 52, no. 1, pp. 1–24, 2003. View at Scopus
  12. P. Tamayo, D. Slonim, J. Mesirov et al., “Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 6, pp. 2907–2912, 1999. View at Publisher · View at Google Scholar
  13. Z. Xiang, T. Todd, K. P. Ku et al., “VIOLIN: vaccine investigation and online information network,” Nucleic Acids Research, vol. 36, no. 1, pp. D923–D928, 2008. View at Publisher · View at Google Scholar · View at PubMed
  14. Y. He, L. Cowell, A. D. Diehl, et al., “VO: vaccine ontology,” in Proceedings of the 1st International Conference on Biomedical Ontology (ICBO '09), Buffalo, NY, USA, August 2009.
  15. R. R. Brinkman, M. Courtot, D. Derom, et al., “Modeling biomedical experimental processes with OBI,” Journal of Biomedical Semantics, vol. 1, supplement 1, p. S7, 2010.
  16. A. Ozgur, Z. Xiang, D. Radev, and Y. He, “Mining of vaccine associated IFN-γ gene interaction networks using the Vaccine Ontology,” Journal of Biomedical Semantics. 2(Suppl 2):S8, 2011, http://www.jbiomedsem.com/qc/content/2/S2/S8.
  17. C. DeLisi and J. A. Berzofsky, “T-cell antigenic sites tend to be amphipathic structures,” Proceedings of the National Academy of Sciences of the United States of America, vol. 82, no. 20, pp. 7048–7052, 1985.
  18. A. S. De Groot and J. A. Berzofsky, “From genome to vaccine—new immunoinformatics tools for vaccine design,” Methods, vol. 34, no. 4, pp. 425–428, 2004. View at Publisher · View at Google Scholar · View at PubMed
  19. A. S. De Groot and L. Moise, “New tools, new approaches and new ideas for vaccine development,” Expert Review of Vaccines, vol. 6, no. 2, pp. 125–127, 2007. View at Publisher · View at Google Scholar · View at PubMed
  20. J. D. Ahlers, I. M. Belyakov, E. K. Thomas, and J. A. Berzofsky, “High-affinity T helper epitope induces complementary helper and APC polarization, increased CTL, and protection against viral infection,” The Journal of Clinical Investigation, vol. 108, no. 11, pp. 1677–1685, 2001. View at Publisher · View at Google Scholar
  21. H. Inaba, W. Martin, A. S. De Groot, S. Qin, and L. J. De Groot, “Thyrotropin receptor epitopes and their relation to histocompatibility leukocyte antigen-DR molecules in graves' disease,” Journal of Clinical Endocrinology and Metabolism, vol. 91, no. 6, pp. 2286–2294, 2006. View at Publisher · View at Google Scholar · View at PubMed
  22. A. S. De Groot, B. M. Jesdale, E. Szu, J. R. Schafer, R. M. Chicz, and G. Deocampo, “An interactive web site providing major histocompatibility ligand predictions: application to HIV research,” AIDS Research and Human Retroviruses, vol. 13, no. 7, pp. 529–531, 1997.
  23. K. B. Bond, B. Sriwanthana, T. W. Hodge et al., “An HLA-directed molecular and bioinformatics approach identifies new HLA-A11 HIV-1 subtype E cytotoxic T lymphocyte epitopes in HIV-1-infected Thais,” AIDS Research and Human Retroviruses, vol. 17, no. 8, pp. 703–717, 2001. View at Publisher · View at Google Scholar · View at PubMed
  24. J. McMurry, H. Sbai, M. L. Gennaro, E. J. Carter, W. Martin, and A. S. De Groot, “Analyzing Mycobacterium tuberculosis proteomes for candidate vaccine epitopes,” Tuberculosis, vol. 85, no. 1-2, pp. 95–105, 2005. View at Publisher · View at Google Scholar · View at PubMed
  25. Y. Dong, S. Demaria, X. Sun et al., “HLA-A2-restricted CD8-cytotoxic-T-Cell responses to novel epitopes in mycobacterium tuberculosis superoxide dismutase, alanine dehydrogenase, and glutamine synthetase,” Infection and Immunity, vol. 72, no. 4, pp. 2412–2415, 2004. View at Publisher · View at Google Scholar
  26. O. A. Koita, D. Dabitao, I. Mahamadou et al., “Confirmation of immunogenic consensus sequence HIV-1 T-cell epitopes in Bamako, Mali and Providence, Rhode Island,” Human Vaccines, vol. 2, no. 3, pp. 119–128, 2006.
  27. A. S. De Groot, “Immunomics: discovering new targets for vaccines and therapeutics,” Drug Discovery Today, vol. 11, no. 5-6, pp. 203–209, 2006. View at Publisher · View at Google Scholar · View at PubMed
  28. J. A. McMurry, S. H. Gregory, L. Moise, D. Rivera, S. Buus, and A. S. De Groot, “Diversity of Francisella tularensis Schu4 antigens recognized by T lymphocytes after natural infections in humans: identification of candidate epitopes for inclusion in a rationally designed tularemia vaccine,” Vaccine, vol. 25, no. 16, pp. 3179–3191, 2007. View at Publisher · View at Google Scholar · View at PubMed
  29. L. Moise, J. A. McMurry, S. Buus, S. Frey, W. D. Martin, and A. S. De Groot, “In silico-accelerated identification of conserved and immunogenic variola/vaccinia T-cell epitopes,” Vaccine, vol. 27, no. 46, pp. 6471–6479, 2009. View at Publisher · View at Google Scholar · View at PubMed
  30. L. Moise, J. A. McMurry, J. Pappo et al., “Identification of genome-derived vaccine candidates conserved between human and mouse-adapted strains of H. pylori,” Human Vaccines, vol. 4, no. 3, pp. 219–223, 2008.
  31. S. K. Kim, M. Cornberg, X. Z. Wang, H. D. Chen, L. K. Selin, and R. M. Welsh, “Private specificities of CD8 T cell responses control patterns of heterologous immunity,” Journal of Experimental Medicine, vol. 201, no. 4, pp. 523–533, 2005. View at Publisher · View at Google Scholar · View at PubMed
  32. R. Rappuoli, “Bridging the knowledge gaps in vaccine design,” Nature Biotechnology, vol. 25, no. 12, pp. 1361–1366, 2007. View at Publisher · View at Google Scholar · View at PubMed
  33. T. Elliott, V. Cerundolo, J. Elvin, and A. Townsend, “Peptide-induced conformational change of the class I heavy chain,” Nature, vol. 351, no. 6325, pp. 402–406, 1991. View at Publisher · View at Google Scholar · View at PubMed
  34. K. Falk, O. Rotzschke, S. Stevanovic, G. Jung, and H. G. Rammensee, “Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules,” Nature, vol. 351, no. 6324, pp. 290–296, 1991. View at Publisher · View at Google Scholar · View at PubMed
  35. O. Rotzschke, K. Falk, S. Stevanovic, G. Jung, P. Walden, and H. G. Rammensee, “Exact prediction of a natural T cell epitope,” European Journal of Immunology, vol. 21, no. 11, pp. 2891–2894, 1991.
  36. E. R. Unanue, “Cellular studies on antigen presentation by class II MHC molecules,” Current Opinion in Immunology, vol. 4, no. 1, pp. 63–69, 1992. View at Publisher · View at Google Scholar
  37. J. H. Brown, T. S. Jardetzky, J. C. Gorga et al., “Three-dimensional structure of the human class II histocompatibility antigen HLA-DR1,” Nature, vol. 364, no. 6432, pp. 33–39, 1993. View at Publisher · View at Google Scholar · View at PubMed
  38. R. M. Chicz, R. G. Urban, J. C. Gorga, D. A. A. Vignali, W. S. Lane, and J. L. Strominger, “Specificity and promiscuity among naturally processed peptides bound to HLA-DR alleles,” Journal of Experimental Medicine, vol. 178, no. 1, pp. 27–47, 1993.
  39. R. N. Germain and D. H. Margulies, “The biochemistry and cell biology and antigen processing and presentation,” Annual Review of Immunology, vol. 11, pp. 403–450, 1993.
  40. P. Cresswell and A. Lanzavecchia, “Antigen processing and recognition,” Current Opinion in Immunology, vol. 13, no. 1, pp. 11–12, 2001. View at Publisher · View at Google Scholar
  41. E. S. Trombetta and I. Mellman, “Cell biology of antigen processing in vitro and in vivo,” Annual Review of Immunology, vol. 23, pp. 975–1028, 2005. View at Publisher · View at Google Scholar · View at PubMed
  42. E. Appella, E. A. Padlan, and D. F. Hunt, “Analysis of the structure of naturally processed peptides bound by class I and class II major histocompatibility complex molecules,” EXS, vol. 73, pp. 105–119, 1995.
  43. R. N. Germain, F. Castellino, R. Han et al., “Processing and presentation of endocytically acquired protein antigens by MHC class II and class I molecules,” Immunological Reviews, no. 151, pp. 5–30, 1996.
  44. A. Sette and J. Sidney, “Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism,” Immunogenetics, vol. 50, no. 3-4, pp. 201–212, 1999.
  45. A. S. De Groot, J. McMurry, and L. Moise, “Prediction of immunogenicity: in silico paradigms, ex vivo and in vivo correlates,” Current Opinion in Pharmacology, vol. 8, no. 5, pp. 620–626, 2008. View at Publisher · View at Google Scholar · View at PubMed
  46. A. S. De Groot, D. S. Rivera, J. A. McMurry, S. Buus, and W. Martin, “Identification of immunogenic HLA-B7 "Achilles' heel" epitopes within highly conserved regions of HIV,” Vaccine, vol. 26, no. 24, pp. 3059–3071, 2008. View at Publisher · View at Google Scholar · View at PubMed
  47. A. S. De Groot, E. A. Bishop, B. Khan et al., “Engineering immunogenic consensus T helper epitopes for a cross-clade HIV vaccine,” Methods, vol. 34, no. 4, pp. 476–487, 2004. View at Publisher · View at Google Scholar · View at PubMed
  48. H. Plotnicky, D. Cyblat-Chanal, J. P. Aubry et al., “The immunodominant influenza matrix t cell epitope recognized in human induces influenza protection in HLA-A2/K transgenic mice,” Virology, vol. 309, no. 2, pp. 320–329, 2003. View at Publisher · View at Google Scholar
  49. P. Wang, J. Sidney, C. Dow, B. Mothé, A. Sette, and B. Peters, “A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach,” PLoS Computational Biology, vol. 4, no. 4, Article ID e1000048, 2008. View at Publisher · View at Google Scholar · View at PubMed
  50. H. G. Rammensee, J. Bachmann, N. P. N. Emmerich, O. A. Bachor, and S. Stevanović, “SYFPEITHI: database for MHC ligands and peptide motifs,” Immunogenetics, vol. 50, no. 3-4, pp. 213–219, 1999.
  51. K. C. Parker, M. A. Bednarek, and J. E. Coligan, “Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains,” Journal of Immunology, vol. 152, no. 1, pp. 163–175, 1994.
  52. A. S. De Groot and W. Martin, “Reducing risk, improving outcomes: bioengineering less immunogenic protein therapeutics,” Clinical Immunology, vol. 131, no. 2, pp. 189–201, 2009. View at Publisher · View at Google Scholar · View at PubMed
  53. H. H. Lin, S. Ray, S. Tongchusak, E. L. Reinherz, and V. Brusic, “Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research,” BMC Immunology, vol. 9, article 8, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  54. H. H. Lin, G. L. Zhang, S. Tongchusak, E. L. Reinherz, and V. Brusic, “Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research,” BMC Bioinformatics, vol. 9, no. 12, article S22, 2008. View at Publisher · View at Google Scholar · View at PubMed
  55. U. Gowthaman and J. N. Agrewala, “In silico tools for predicting peptides binding to HLA-class II molecules: more confusion than conclusion,” Journal of Proteome Research, vol. 7, no. 1, pp. 154–163, 2008. View at Publisher · View at Google Scholar · View at PubMed
  56. P. Wang, J. Sidney, Y. Kim et al., “Peptide binding predictions for HLA DR, DP and DQ molecules,” BMC Bioinformatics, vol. 11, 2010. View at Publisher · View at Google Scholar · View at PubMed
  57. S. Bulik, B. Peters, C. Ebeling, and H. Holzhütter, “Cytosolic processing of proteasomal cleavage products can enhance the presentation efficiency of MHC-1 epitopes,” Genome Informatics, vol. 15, no. 1, pp. 24–34, 2004.
  58. B. Peters, S. Bulik, R. Tampe, P. M. Van Endert, and H. G. Holzhütter, “Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors,” Journal of Immunology, vol. 171, no. 4, pp. 1741–1749, 2003.
  59. M. Nielsen, C. Lundegaard, O. Lund, and C. Keşmir, “The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage,” Immunogenetics, vol. 57, no. 1-2, pp. 33–41, 2005. View at Publisher · View at Google Scholar · View at PubMed
  60. M. V. Larsen, C. Lundegaard, K. Lamberth, S. Buus, O. Lund, and M. Nielsen, “Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction,” BMC Bioinformatics, vol. 8, article 424, 2007. View at Publisher · View at Google Scholar · View at PubMed
  61. S. Tenzer, B. Peters, S. Bulik et al., “Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding,” Cellular and Molecular Life Sciences, vol. 62, no. 9, pp. 1025–1037, 2005. View at Publisher · View at Google Scholar · View at PubMed
  62. T. Stranzl, M. V. Larsen, C. Lundegaard, and M. Nielsen, “NetCTLpan: pan-specific MHC class I pathway epitope predictions,” Immunogenetics, vol. 62, no. 6, pp. 357–368, 2010. View at Publisher · View at Google Scholar · View at PubMed
  63. A. B. Riemer, D. B. Keskin, G. Zhang, et al., “A conserved E7-derived cytotoxic T lymphocyte epitope expressed on human papillomavirus 16-transformed HLA-A2+ epithelial cancers,” The Journal of Biological Chemistry, vol. 285, pp. 29608–29622, 2010.
  64. D. Enshell-Seijffers, D. Denisov, B. Groisman et al., “The mapping and reconstitution of a conformational discontinuous B-cell epitope of HIV-1,” Journal of Molecular Biology, vol. 334, no. 1, pp. 87–101, 2003. View at Publisher · View at Google Scholar
  65. A. Schreiber, M. Humbert, A. Benz, and U. Dietrich, “3D-Epitope-Explorer (3DEX): localization of conformational epitopes within three-dimensional structures of proteins,” Journal of Computational Chemistry, vol. 26, no. 9, pp. 879–887, 2005. View at Publisher · View at Google Scholar · View at PubMed
  66. U. Kulkarni-Kale, S. Bhosle, and A. S. Kolaskar, “CEP: a conformational epitope prediction server,” Nucleic Acids Research, vol. 33, no. 2, pp. W168–W171, 2005. View at Publisher · View at Google Scholar · View at PubMed
  67. M. J. Sweredoski and P. Baldi, “PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure,” Bioinformatics, vol. 24, no. 12, pp. 1459–1460, 2008. View at Publisher · View at Google Scholar · View at PubMed
  68. D. J. Barlow, M. S. Edwards, and J. M. Thornton, “Continuous and discontinuous protein antigenic determinants,” Nature, vol. 322, no. 6081, pp. 747–748, 1986.
  69. M. J. Blythe and D. R. Flower, “Benchmarking B cell epitope prediction: underperformance of existing methods,” Protein Science, vol. 14, no. 1, pp. 246–248, 2005. View at Publisher · View at Google Scholar · View at PubMed
  70. U. Reimer, “Prediction of linear B-cell epitopes,” Methods in Molecular Biology, vol. 524, pp. 335–344, 2009. View at Publisher · View at Google Scholar
  71. E. Rajnavolgyi, N. Nagy, B. Thuresson, et al., “A repetitive sequence of Epstein-Barr virus nuclear antigen 6 comprises overlapping T cell epitopes which induce HLA-DR-restricted CD4(+) T lymphocytes,” International Immunology, vol. 12, no. 3, pp. 281–293, 2000.
  72. C. M. Graham, B. C. Barnett, I. Hartlmayr et al., “The structural requirements for class II (I-A(d))-restricted T cell recognition of influenza hemagglutinin: b cell epitopes define T cell epitopes,” European Journal of Immunology, vol. 19, no. 3, pp. 523–528, 1989.
  73. W. Fischer, S. Perkins, J. Theiler et al., “Polyvalent vaccines for optimal coverage of potential T-cell epitopes in global HIV-1 variants,” Nature Medicine, vol. 13, no. 1, pp. 100–106, 2007. View at Publisher · View at Google Scholar · View at PubMed
  74. A. S. De Groot, L. Marcon, E. A. Bishop et al., “HIV vaccine development by computer assisted design: the GAIA vaccine,” Vaccine, vol. 23, no. 17-18, pp. 2136–2148, 2005. View at Publisher · View at Google Scholar · View at PubMed
  75. B. Gaschen, J. Taylor, K. Yusim et al., “Diversity considerations in HIV-1 vaccine selection,” Science, vol. 296, no. 5577, pp. 2354–2360, 2002. View at Publisher · View at Google Scholar · View at PubMed
  76. F. Gao, B. T. Korber, E. A. Weaver, H. X. Liao, B. H. Hahn, and B. F. Haynes, “Centralized immunogens as a vaccine strategy to overcome HIV-1 diversity,” Expert Review of Vaccines, vol. 3, no. 4, pp. S161–S168, 2004. View at Publisher · View at Google Scholar
  77. F. Gao, E. A. Weaver, Z. Lu et al., “Antigenicity and immunogenicity of a synthetic human immunodeficiency virus type 1 group M consensus envelope glycoprotein,” Journal of Virology, vol. 79, no. 2, pp. 1154–1163, 2005. View at Publisher · View at Google Scholar · View at PubMed
  78. D. C. Nickle, M. Rolland, M. A. Jensen et al., “Coping with viral diversity in HIV vaccine design,” PLoS Computational Biology, vol. 3, no. 4, article e75, pp. 754–762, 2007. View at Publisher · View at Google Scholar · View at PubMed
  79. L. A. McNamara, Y. He, and Z. Yang, “Using epitope predictions to evaluate efficacy and population coverage of the Mtb72f vaccine for tuberculosis,” BMC Immunology, vol. 11, article 18, 2010. View at Publisher · View at Google Scholar · View at PubMed
  80. J. Söllner, A. Heinzel, G. Summer et al., “Concept and application of a computational vaccinology workflow,” Immunome Research, vol. 6, supplement 2, 2010. View at Publisher · View at Google Scholar · View at PubMed
  81. F. Pappalardo, M. D. Halling-Brown, N. Rapin et al., “ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization,” Briefings in Bioinformatics, vol. 10, no. 3, pp. 330–340, 2009. View at Publisher · View at Google Scholar · View at PubMed
  82. M. Feldhahn, P. Dönnes, P. Thiel, and O. Kohlbacher, “FRED—a framework for T-cell epitope detection,” Bioinformatics, vol. 25, no. 20, pp. 2758–2759, 2009. View at Publisher · View at Google Scholar · View at PubMed
  83. L. Moise, J. A. McMurry, J. Pappo et al., “Identification of genome-derived vaccine candidates conserved between human and mouse-adapted strains of H. pylori,” Human Vaccines, vol. 4, no. 3, pp. 219–223, 2008.
  84. S. H. Gregory, S. Mott, J. Phung et al., “Epitope-based vaccination against pneumonic tularemia,” Vaccine, vol. 27, no. 39, pp. 5299–5306, 2009. View at Publisher · View at Google Scholar · View at PubMed
  85. A. L. Delcher, D. Harmon, S. Kasif, O. White, and S. L. Salzberg, “Improved microbial gene identification with GLIMMER,” Nucleic Acids Research, vol. 27, no. 23, pp. 4636–4641, 1999.
  86. D. Frishman, A. Mironov, H. W. Mewes, and M. Gelfand, “Combining diverse evidence for gene recognition in completely sequenced bacterial genomes,” Nucleic Acids Research, vol. 26, no. 12, pp. 2941–2947, 1998. View at Publisher · View at Google Scholar
  87. J. Besemer, A. Lomsadze, and M. Borodovsky, “GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions,” Nucleic Acids Research, vol. 29, no. 12, pp. 2607–2618, 2001.
  88. S. T. Fitz-Gibbon, H. Ladner, U. J. Kim, K. O. Stetter, M. I. Simon, and J. H. Miller, “Genome sequence of the hyperthermophilic crenarchaeon Pyrobaculum aerophilum,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 2, pp. 984–989, 2002. View at Publisher · View at Google Scholar · View at PubMed
  89. A. M. Cerdeño-Tárraga, A. Efstratiou, L. G. Dover et al., “The complete genome sequence and analysis of Corynebacterium diphtheriae NCTC13129,” Nucleic Acids Research, vol. 31, no. 22, pp. 6516–6523, 2003. View at Publisher · View at Google Scholar
  90. J. Wei, M. B. Goldberg, V. Burland et al., “Complete genome sequence and comparative genomics of Shigella flexneri serotype 2a strain 2457T,” Infection and Immunity, vol. 71, no. 5, pp. 2775–2786, 2003. View at Publisher · View at Google Scholar
  91. M. P. McLeod, X. Qin, S. E. Karpathy et al., “Complete genome sequence of Rickettsia typhi and comparison with sequences of other rickettsiae,” Journal of Bacteriology, vol. 186, no. 17, pp. 5842–5855, 2004. View at Publisher · View at Google Scholar · View at PubMed
  92. P. Stothard and D. S. Wishart, “Automated bacterial genome analysis and annotation,” Current Opinion in Microbiology, vol. 9, no. 5, pp. 505–510, 2006. View at Publisher · View at Google Scholar · View at PubMed
  93. A. Economou, P. J. Christie, R. C. Fernandez, T. Palmer, G. V. Plano, and A. P. Pugsley, “Secretion by numbers: protein traffic in prokaryotes,” Molecular Microbiology, vol. 62, no. 2, pp. 308–319, 2006. View at Publisher · View at Google Scholar · View at PubMed
  94. T. T. Tseng, B. M. Tyler, and J. C. Setubal, “Protein secretion systems in bacterial-host associations, and their description in the Gene Ontology,” BMC Microbiology, vol. 9, no. 1, article S2, 2009. View at Publisher · View at Google Scholar · View at PubMed
  95. H. Tjalsma, A. Bolhuis, J. D. H. Jongbloed, S. Bron, and J. M. Van Dijl, “Signal peptide-dependent protein transport in Bacillus subtilis: a genome-based survey of the secretome,” Microbiology and Molecular Biology Reviews, vol. 64, no. 3, pp. 515–547, 2000.
  96. H. Tjalsma, H. Antelmann, J. D. H. Jongbloed et al., “Proteomics of protein secretion by Bacillus subtilis: separating the “secrets” of the secretome,” Microbiology and Molecular Biology Reviews, vol. 68, no. 2, pp. 207–233, 2004. View at Publisher · View at Google Scholar · View at PubMed
  97. J. L. Gardy and F. S. L. Brinkman, “Methods for predicting bacterial protein subcellular localization,” Nature Reviews Microbiology, vol. 4, no. 10, pp. 741–751, 2006. View at Publisher · View at Google Scholar · View at PubMed
  98. C. S. Yu, C. J. Lin, and J. K. Hwang, “Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions,” Protein Science, vol. 13, no. 5, pp. 1402–1406, 2004. View at Publisher · View at Google Scholar · View at PubMed
  99. Z. Lu, D. Szafron, R. Greiner et al., “Predicting subcellular localization of proteins using machine-learned classifiers,” Bioinformatics, vol. 20, no. 4, pp. 547–556, 2004. View at Publisher · View at Google Scholar
  100. A. P. Tampakaki, V. E. Fadouloglou, A. D. Gazi, N. J. Panopoulos, and M. Kokkinidis, “Conserved features of type III secretion,” Cellular Microbiology, vol. 6, no. 9, pp. 805–816, 2004. View at Publisher · View at Google Scholar · View at PubMed
  101. P. D. Schloss and JO. Handelsman, “Status of the microbial census,” Microbiology and Molecular Biology Reviews, vol. 68, no. 4, pp. 686–691, 2004. View at Publisher · View at Google Scholar · View at PubMed
  102. F. M. Cohan and E. B. Perry, “A Systematics for Discovering the Fundamental Units of Bacterial Diversity,” Current Biology, vol. 17, no. 10, pp. R373–R386, 2007. View at Publisher · View at Google Scholar · View at PubMed
  103. N. T. Perna, G. Plunkett, V. Burland et al., “Genome sequence of enterohaemorrhagic Escherichia coli O157:H7,” Nature, vol. 409, no. 6819, pp. 529–533, 2001. View at Publisher · View at Google Scholar · View at PubMed
  104. Y. Zhang, C. Laing, M. Steele et al., “Genome evolution in major Escherichia coli O157:H7 lineages,” BMC Genomics, vol. 8, 2007. View at Publisher · View at Google Scholar · View at PubMed
  105. M. Brochet, E. Couvé, M. Zouine et al., “Genomic diversity and evolution within the species Streptococcus agalactiae,” Microbes and Infection, vol. 8, no. 5, pp. 1227–1243, 2006. View at Publisher · View at Google Scholar · View at PubMed
  106. H. Tettelin, V. Masignani, M. J. Cieslewicz et al., “Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 39, pp. 13950–13955, 2005. View at Publisher · View at Google Scholar · View at PubMed
  107. E. Brzuszkiewicz, H. Brüggemann, H. Liesegang et al., “How to become a uropathogen: comparative genomic analysis of extraintestinal pathogenic Escherichia coli strains,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 34, pp. 12879–12884, 2006. View at Publisher · View at Google Scholar · View at PubMed
  108. H. Tettelin, D. Medini, C. Donati, and V. Masignani, “Towards a universal group B Streptococcus vaccine using multistrain genome analysis,” Expert Review of Vaccines, vol. 5, no. 5, pp. 687–694, 2006. View at Publisher · View at Google Scholar · View at PubMed
  109. D. Maione, I. Margarit, C. D. Rinaudo et al., “Immunology: identification of a universal group B Streptococcus vaccine by multiple genome screen,” Science, vol. 309, no. 5731, pp. 148–150, 2005. View at Publisher · View at Google Scholar · View at PubMed
  110. Y. He, Z. Xiang, and H. L.T. Mobley, “Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development,” Journal of Biomedicine and Biotechnology, vol. 2010, Article ID 297505, 2010 pages, 2010. View at Publisher · View at Google Scholar · View at PubMed
  111. H. Ochman and N. A. Moran, “Genes lost and genes found: evolution of bacterial pathogenesis and symbiosis,” Science, vol. 292, no. 5519, pp. 1096–1098, 2001. View at Publisher · View at Google Scholar
  112. G. Sachdeva, K. Kumar, P. Jain, and S. Ramachandran, “SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks,” Bioinformatics, vol. 21, no. 4, pp. 483–491, 2005. View at Publisher · View at Google Scholar · View at PubMed
  113. S. Vivona, F. Bernante, and F. Filippini, “NERVE: new enhanced reverse vaccinology environment,” BMC Biotechnology, vol. 6, article 35, 2006. View at Publisher · View at Google Scholar · View at PubMed
  114. Y. He and Z. Xiang, “Bioinformatics analysis of Brucella vaccines and vaccine targets using VIOLIN,” Immunome Research, vol. 6, supplement 1, p. S5, 2010. View at Publisher · View at Google Scholar · View at PubMed
  115. Z. Xiang and Y. He, “Procedia in vaccinology,” in Proceedings of the 2nd Global Congress on Vaccines, vol. 1, pp. 23–29, Boston, Mass, USA, 2009.
  116. D. Serruto and R. Rappuoli, “Post-genomic vaccine development,” The FEBS Letters, vol. 580, no. 12, pp. 2985–2992, 2006. View at Publisher · View at Google Scholar · View at PubMed
  117. M. Mora, C. Donati, D. Medini, A. Covacci, and R. Rappuoli, “Microbial genomes and vaccine design: refinements to the classical reverse vaccinology approach,” Current Opinion in Microbiology, vol. 9, no. 5, pp. 532–536, 2006. View at Publisher · View at Google Scholar · View at PubMed
  118. E. L. Hendrickson, R. J. Lamont, and M. Hackett, “Tools for interpreting large-scale protein profiling in microbiology,” Journal of Dental Research, vol. 87, no. 11, pp. 1004–1015, 2008. View at Publisher · View at Google Scholar
  119. Y. Liang and A. Kelemen, “Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments,” Functional and Integrative Genomics, vol. 6, no. 1, pp. 1–13, 2006. View at Publisher · View at Google Scholar · View at PubMed
  120. Affymetrix, GeneChip Expression Data Analysis Fundamentals, Affymetrix, Inc., Santa Clara, Calif, USA, 2004.
  121. R. A. Irizarry, B. Hobbs, F. Collin et al., “Exploration, normalization, and summaries of high density oligonucleotide array probe level data,” Biostatistics, vol. 4, no. 2, pp. 249–264, 2003.
  122. C. Li and W. H. Wong, “Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 1, pp. 31–36, 2001. View at Publisher · View at Google Scholar · View at PubMed
  123. R. C. Gentleman, V. J. Carey, D. M. Bates et al., “Bioconductor: open software development for computational biology and bioinformatics,” Genome Biology, vol. 5, no. 10, p. R80, 2004.
  124. W. Pan, “A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments,” Bioinformatics, vol. 18, no. 4, pp. 546–554, 2002.
  125. J. F. Ayroles and G. Gibson, “Analysis of variance of microarray data,” Methods in Enzymology, vol. 411, pp. 214–233, 2006. View at Publisher · View at Google Scholar · View at PubMed
  126. V. G. Tusher, R. Tibshirani, and G. Chu, “Significance analysis of microarrays applied to the ionizing radiation response,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 9, pp. 5116–5121, 2001. View at Publisher · View at Google Scholar · View at PubMed
  127. G. K. Smyth, “Linear models and empirical bayes methods for assessing differential expression in microarray experiments,” Statistical Applications in Genetics and Molecular Biology, vol. 3, no. 1, article 3, 2004.
  128. S. Raychaudhuri, P. D. Sutphin, J. T. Chang, and R. B. Altman, “Basic microarray analysis: grouping and feature reduction,” Trends in Biotechnology, vol. 19, no. 5, pp. 189–193, 2001. View at Publisher · View at Google Scholar
  129. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, “Cluster analysis and display of genome-wide expression patterns,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 25, pp. 14863–14868, 1998. View at Publisher · View at Google Scholar
  130. Z. Xiang, Z. S. Qin, and Y. He, “CRCView: a web server for analyzing and visualizing microarray gene expression data using model-based clustering,” Bioinformatics, vol. 23, no. 14, pp. 1843–1845, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  131. D. A. Hosack, G. Dennis Jr., B. T. Sherman, H. C. Lane, and R. A. Lempicki, “Identifying biological themes within lists of genes with EASE,” Genome Biology, vol. 4, no. 10, p. R70, 2003. View at Scopus
  132. A. Bild and P. G. Febbo, “Application of a priori established gene sets to discover biologically important differential expression in microarray data,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 43, pp. 15278–15279, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  133. D. A. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  134. T. Beißbarth and T. P. Speed, “GOstat: find statistically overrepresented Gene Ontologies with a group of genes,” Bioinformatics, vol. 20, no. 9, pp. 1464–1465, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  135. Y. U. Xia, H. Yu, R. Jansen et al., “Analyzing cellular biochemistry in terms of molecular networks,” Annual Review of Biochemistry, vol. 73, pp. 1051–1087, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  136. J. Goutsias and N. H. Lee, “Computational and experimental approaches for modeling gene regulatory networks,” Current Pharmaceutical Design, vol. 13, no. 14, pp. 1415–1436, 2007. View at Publisher · View at Google Scholar · View at Scopus
  137. J. Hardin, A. Mitani, L. Hicks, and B. VanKoten, “A robust measure of correlation between two genes on a microarray,” BMC Bioinformatics, vol. 8, Article 220, 2007. View at Publisher · View at Google Scholar · View at PubMed
  138. P. A. Morel, S. Ta'asan, B. F. Morel, D. E. Kirschner, and J. L. Flynn, “New insights into mathematical modeling of the immune system,” Immunologic Research, vol. 36, no. 1–3, pp. 157–165, 2006. View at Scopus
  139. G. Dreyfus, Neural Networks : Methodology and Applications, Springer, New York, NY, USA, 2005.
  140. N. Friedman, M. Linial, I. Nachman, and D. Pe'er, “Using Bayesian networks to analyze expression data,” Journal of Computational Biology, vol. 7, no. 3-4, pp. 601–620, 2000. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  141. Z. Xiang, R. M. Minter, X. Bi, P. J. Woolf, and Y. He, “miniTUBA: medical inference by network integration of temporal data using Bayesian analysis,” Bioinformatics, vol. 23, no. 18, pp. 2423–2432, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  142. A. Stuart, J. K. Ord, and S. F. Arnold, Kendall's Advanced Theory of Statistics, Oxford University Press, New York, NY, USA, 2004.
  143. J. Yu, V. A. Smith, P. P. Wang, A. J. Hartemink, and E. D. Jarvis, “Advances to Bayesian network inference for generating causal networks from observational biological data,” Bioinformatics, vol. 20, no. 18, pp. 3594–3603, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  144. N. Dhiman, R. Bonilla, D. O'Kane J, and G. A. Poland, “Gene expression microarrays: a 21st century tool for directed vaccine design,” Vaccine, vol. 20, no. 1-2, pp. 22–30, 2001. View at Publisher · View at Google Scholar · View at Scopus
  145. T. Chen, “DNA microarrays—an armory for combating infectious diseases in the new century,” Infectious Disorders—Drug Targets, vol. 6, no. 3, pp. 263–279, 2006. View at Publisher · View at Google Scholar · View at Scopus
  146. J. A. Young, Q. L. Fivelman, P. L. Blair et al., “The Plasmodium falciparum sexual development transcriptome: a microarray analysis using ontology-based pattern identification,” Molecular and Biochemical Parasitology, vol. 143, no. 1, pp. 67–79, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  147. T. Sturniolo, E. Bono, J. Ding et al., “Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices,” Nature Biotechnology, vol. 17, no. 6, pp. 555–561, 1999. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  148. T. D. Querec, R. S. Akondy, E. K. Lee et al., “Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans,” Nature Immunology, vol. 10, no. 1, pp. 116–125, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  149. D. Gaucher, R. Therrien, N. Kettaf et al., “Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses,” Journal of Experimental Medicine, vol. 205, no. 13, pp. 3119–3131, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  150. B. Pulendran, J. Miller, T. D. Querec et al., “Case of yellow fever vaccine-associated viscerotropic disease with prolonged viremia, robust adaptive immune responses, and polymorphisms in CCR5 and RANTES genes,” Journal of Infectious Diseases, vol. 198, no. 4, pp. 500–507, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  151. B. A. McKinney, D. M. Reif, M. T. Rock et al., “Cytokine expression patterns associated with systemic adverse events following smallpox immunization,” Journal of Infectious Diseases, vol. 194, no. 4, pp. 444–453, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  152. I. Hamaguchi, J.-i. Imai, H. Momose et al., “Two vaccine toxicity-related genes Agp and Hpx could prove useful for pertussis vaccine safety control,” Vaccine, vol. 25, no. 17, pp. 3355–3364, 2007. View at Publisher · View at Google Scholar · View at PubMed
  153. T. Mizukami, J. I. Imai, I. Hamaguchi et al., “Application of DNA microarray technology to influenza A/Vietnam/1194/2004 (H5N1) vaccine safety evaluation,” Vaccine, vol. 26, no. 18, pp. 2270–2283, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  154. V. Brusic and N. Petrovsky, “Immunoinformatics and its relevance to understanding human immune disease,” Expert Review of Clinical Immunology, vol. 1, pp. 145–157, 2005.
  155. C. A.A. Beauchemin and A. Handel, “A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead,” BMC Public Health, vol. 11, supplement 1, p. S7, 2011. View at Publisher · View at Google Scholar · View at PubMed
  156. D. E. Kirschner, S. T. Chang, T. W. Riggs, N. Perry, and J. J. Linderman, “Toward a multiscale model of antigen presentation in immunity,” Immunological Reviews, vol. 216, no. 1, pp. 93–118, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  157. M. A. Fishman and A. S. Perelson, “Th1/Th2 cross regulation,” Journal of Theoretical Biology, vol. 170, no. 1, pp. 25–56, 1994. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  158. B. F. Keele, E. E. Giorgi, J. F. Salazar-Gonzalez et al., “Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 21, pp. 7552–7557, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  159. S. Marino, J. J. Linderman, and D. E. Kirschner, “A multifaceted approach to modeling the immune response in tuberculosis,” Wiley Interdisciplinary Reviews. Systems Biology and Medicine, In Press. View at Publisher · View at Google Scholar · View at PubMed
  160. M. P. Davenport, R. M. Ribeiro, L. Zhang, D. P. Wilson, and A. S. Perelson, “Understanding the mechanisms and limitations of immune control of HIV,” Immunological Reviews, vol. 216, no. 1, pp. 164–175, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  161. J. Schulze-Horsel, M. Schulze, G. Agalaridis, Y. Genzel, and U. Reichl, “Infection dynamics and virus-induced apoptosis in cell culture-based influenza vaccine production-Flow cytometry and mathematical modeling,” Vaccine, vol. 27, no. 20, pp. 2712–2722, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  162. S. Y. Kim and S. J. Goldie, “Cost-effectiveness analyses of vaccination programmes: a focused review of modelling approaches,” PharmacoEconomics, vol. 26, no. 3, pp. 191–215, 2008. View at Scopus
  163. S. J. Goldie, J. J. Kim, K. Kobus et al., “Cost-effectiveness of HPV 16, 18 vaccination in Brazil,” Vaccine, vol. 25, no. 33, pp. 6257–6270, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  164. S. Aballéa, J. Chancellor, M. Martin et al., “The cost-effectiveness of influenza vaccination for people aged 50 to 64 years: an international model,” Value in Health, vol. 10, no. 2, pp. 98–116, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  165. D. J. Isaacman, D. R. Strutton, E. A. Kalpas et al., “The impact of indirect (herd) protection on the cost-effectiveness of pneumococcal conjugate vaccine,” Clinical Therapeutics, vol. 30, no. 2, pp. 341–357, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  166. A. Palladini, G. Nicoletti, F. Pappalardo, et al., “In silico modeling and in vivo efficacy of cancer-preventive vaccinations,” Cancer Research, vol. 70, pp. 7775–7763, 2010.
  167. M. Pennisi, F. Pappalardo, A. Palladini et al., “Modeling the competition between lung metastases and the immune system using agents,” BMC Bioinformatics, vol. 11, supplement 7, article S13, 2010. View at Publisher · View at Google Scholar · View at PubMed
  168. F. Pappalardo, P. L. Lollini, F. Castiglione, and S. Motta, “Modeling and simulation of cancer immunoprevention vaccine,” Bioinformatics, vol. 21, no. 12, pp. 2891–2897, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  169. P.-L. Lollini, S. Motta, and F. Pappalardo, “Discovery of cancer vaccination protocols with a genetic algorithm driving an agent based simulator,” BMC Bioinformatics, vol. 7, article 352, 2006. View at Publisher · View at Google Scholar · View at PubMed
  170. F. Pappalardo, M. Pennisi, F. Castiglione, and S. Motta, “Vaccine protocols optimization: in silico experiences,” Biotechnology Advances, vol. 28, no. 1, pp. 82–93, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  171. S. M. Blower, K. Koelle, D. E. Kirschner, and J. Mills, “Live attenuated HIV vaccines: predicting the tradeoff between efficacy and safety,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 6, pp. 3618–3623, 2001. View at Publisher · View at Google Scholar · View at PubMed
  172. T. J. John and R. Samuel, “Herd immunity and herd effect: new insights and definitions,” European Journal of Epidemiology, vol. 16, no. 7, pp. 601–606, 2000. View at Publisher · View at Google Scholar · View at Scopus
  173. “Recommendations of the International Task Force for Disease Eradication,” MMWR—Recommendations and Reports, vol. 42, no. 16, pp. 1–38, 1993.
  174. National Center for Immunization and Respiratory Diseases, “General recommendations on immunization—recommendations of the Advisory Committee on Immunization Practices (ACIP),” MMWR—Recommendations and Reports, vol. 60, no. 2, pp. 1–64, 2011.
  175. D. Wood, K. N. Saarlas, M. Inkelas, and B. T. Matyas, “Immunization registries in the United States: implications for the practice of public health in a changing health care system,” Annual Review of Public Health, vol. 20, pp. 231–255, 1999. View at Publisher · View at Google Scholar · View at PubMed
  176. R. T. Chen, J. W. Glasser, P. H. Rhodes et al., “Vaccine safety datalink project: a new tool for improving vaccine safety monitoring in the United States,” Pediatrics, vol. 99, no. 6, pp. 765–773, 1997. View at Publisher · View at Google Scholar · View at Scopus
  177. B. P. Hull, S. L. Deeks, and P. B. McIntyre, “The Australian Childhood Immunisation Register-A model for universal immunisation registers?” Vaccine, vol. 27, no. 37, pp. 5054–5060, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  178. “National standards for immunization coverage assessment: recommendations from the Canadian Immunization Registry Network,” Canada Communicable Disease Report, vol. 31, pp. 93–97, 2005.
  179. C. Trewin, H. B. Strand, and E. K. Grøholt, “Norhealth: norwegian health information system,” Scandinavian Journal of Public Health, vol. 36, no. 7, pp. 685–689, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  180. M. L. Popovich, J. J. Aramini, and M. Garcia, “Immunizations: the first step in a personal health record to empower patients,” Stud Health Technol Inform, vol. 137, pp. 286–295, 2008.
  181. D. B. Fishbein, B. C. Willis, W. M. Cassidy et al., “Determining indications for adult vaccination: patient self-assessment, medical record, or both?” Vaccine, vol. 24, no. 6, pp. 803–818, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  182. J. A. Boom, A. C. Dragsbaek, and C. S. Nelson, “The success of an immunization information system in the wake of Hurricane Katrina,” Pediatrics, vol. 119, no. 6, pp. 1213–1217, 2007. View at Publisher · View at Google Scholar · View at PubMed
  183. K. A. Feemster, C. V. Spain, M. Eberhart, S. Pati, and B. Watson, “Identifying infants at increased risk for late initiation of immunizations: maternal and provider characteristics,” Public Health Reports, vol. 124, no. 1, pp. 42–53, 2009. View at Scopus
  184. F. Wei, J. P. Mullooly, M. Goodman et al., “Identification and characteristics of vaccine refusers,” BMC Pediatrics, vol. 9, no. 1, article 18, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  185. V. L. Hinrichsen, B. Kruskal, M. A. O'Brien, T. A. Lieu, and R. Platt, “Using electronic medical records to enhance detection and reporting of vaccine adverse events,” Journal of the American Medical Informatics Association, vol. 14, no. 6, pp. 731–735, 2007. View at Publisher · View at Google Scholar · View at PubMed
  186. A. R. Hinman, G. A. Urquhart, R. A. Strikas et al., “Immunization information systems: national vaccine advisory committee progress report, 2007,” Journal of Public Health Management and Practice, vol. 13, no. 6, pp. 553–558, 2007. View at Publisher · View at Google Scholar · View at PubMed
  187. C. P. Farrington and E. Miller, “Vaccine trials,” Molecular Biotechnology, vol. 17, no. 1, pp. 43–58, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  188. D. S. Fedson, “Measuring protection: efficacy versus effectiveness,” Developments in biological standardization, vol. 95, pp. 195–201, 1998. View at Scopus
  189. R. T. Chen, S. C. Rastogi, J. R. Mullen et al., “The vaccine adverse event reporting system (VAERS),” Vaccine, vol. 12, no. 6, pp. 542–550, 1994. View at Publisher · View at Google Scholar · View at Scopus
  190. K. S. Lankinen, S. Pastila, T. Kilpi, H. Nohynek, P. H. Mäkelä, and P. Olin, “Vaccinovigilance in Europe—need for timeliness, standardization and resource,” Bulletin of the World Health Organization, vol. 82, no. 11, pp. 828–835, 2004.
  191. D. Banks, E. J. Woo, D. R. Burwen, P. Perucci, M. M. Braun, and R. Ball, “Comparing data mining methods on the VAERS database,” Pharmacoepidemiology and Drug Safety, vol. 14, no. 9, pp. 601–609, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  192. T. Verstraeten, F. DeStefano, R. T. Chen, and E. Miller, “Vaccine safety surveillance using large linked databases: opportunities, hazards and proposed guidelines,” Expert Review of Vaccines, vol. 2, no. 1, pp. 21–29, 2003. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  193. J. Baggs, J. Gee, E. Lewis, et al., “The Vaccine Safety Datalink: a model for monitoring immunization safety,” Pediatrics, vol. 127, supplement 1, pp. S45–S53, 2011. View at Publisher · View at Google Scholar · View at PubMed
  194. P. Kramarz, E. K. France, F. Destefano et al., “Population-based study of rotavirus vaccination and intussusception,” Pediatric Infectious Disease Journal, vol. 20, no. 4, pp. 410–416, 2001. View at Publisher · View at Google Scholar · View at Scopus
  195. W. W. Thompson, C. Price, B. Goodson et al., “Early thimerosal exposure and neuropsychological outcomes at 7 to 10 years,” The New England Journal of Medicine, vol. 357, no. 13, pp. 1281–1292, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  196. F. DeStefano, T. Verstraeten, L. A. Jackson, et al., “Vaccinations and risk of central nervous system demyelinating diseases in adults,” Archives of Neurology, vol. 60, pp. 504–509, 2003.
  197. P. Farrington, S. Pugh, A. Colville et al., “A new method for active surveillance of adverse events from diphtheria/tetanus/pertussis and measles/mumps/rubella vaccines,” The Lancet, vol. 345, no. 8949, pp. 567–569, 1995. View at Scopus
  198. M. Ali, C. G. Do, J. D. Clemens et al., “The use of a computerized database to monitor vaccine safety in Viet Nam,” Bulletin of the World Health Organization, vol. 83, no. 8, pp. 604–610, 2005.
  199. D. Tapscott, Wikinomics, Penguin, New York, NY, USA, 2008.
  200. V. E. Pitzer, C. Viboud, L. Simonsen et al., “Demographic variability, vaccination, and the spatiotemporal dynamics of rotavirus epidemics,” Science, vol. 325, no. 5938, pp. 290–294, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  201. C. G. Whitney, M. M. Farley, J. Hadler et al., “Decline in invasive pneumococcal disease after the introduction of protein-polysaccharide conjugate vaccine,” The New England Journal of Medicine, vol. 348, no. 18, pp. 1737–1746, 2003. View at Publisher · View at Google Scholar · View at PubMed
  202. S. O. Sow, M. D. Tapia, S. Diallo et al., “Haemophilus influenzae type b conjugate vaccine introduction in Mali: impact on disease burden and serologic correlate of protection,” American Journal of Tropical Medicine and Hygiene, vol. 80, no. 6, pp. 1033–1038, 2009. View at Scopus
  203. R. T. Chen, R. Weierbach, Z. Bisoffi et al., “A 'post-honeymoon period' measles outbreak in Muyinga sector, Burundi,” International Journal of Epidemiology, vol. 23, no. 1, pp. 185–193, 1994. View at Scopus
  204. R. T. Chen, I. R. Hardy, P. H. Rhodes, D. K. Tyshchenko, A.V. Moiseeva, and V. F. Marievsky, “Ukraine, 1992: first assessment of diphtheria vaccine effectiveness during the recent resurgence of diphtheria in the Former Soviet Union,” Journal of Infectious Diseases, vol. 181, supplement 1, pp. S178–S183, 2000.
  205. N. C. Grassly, J. Wenger, S. Durrani et al., “Protective efficacy of a monovalent oral type 1 poliovirus vaccine: a case-control study,” The Lancet, vol. 369, no. 9570, pp. 1356–1362, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  206. W. H. McNeill, Plagues and Peoples, Blackwell Scientific Publications, Oxford, UK, 1976.
  207. M. A. Kermack, “A contribution to the mathematical theory of epidemics,” Proceedings of the Royal Society of London. Series A, vol. 115, pp. 700–721, 1927.
  208. R. M. Anderson and R. M. May, “Directly transmitted infectious diseases: control by vaccination,” Science, vol. 215, no. 4536, pp. 1053–1060, 1982. View at Scopus
  209. R. M. Anderson, R. M. May, and B. Anderson, Infectious Diseases of Humans: Dynamics and Control, Oxford University Press, Oxford, UK, 1991.
  210. K. Ellen and S. M. Cromley, GIS and Public Health, Guilford Press, New York, NY, USA, 2002.
  211. N. J. Gay, “The theory of measles elimination: implications for the design of elimination strategies,” Journal of Infectious Diseases, vol. 189, no. 1, pp. S27–S35, 2004. View at Scopus
  212. H. R. Babad, D. J. Nokes, N. J. Gay, E. Miller, P. Morgan-Capner, and R. M. Anderson, “Predicting the impact of measles vaccination in England and Wales: model validation and analysis of policy options,” Epidemiology and Infection, vol. 114, no. 2, pp. 319–344, 1995. View at Scopus
  213. J. M. Best, C. Castillo-Solorzano, J. S. Spika et al., “Reducing the global burden of congenital rubella syndrome: report of the World Health Organization Steering Committee on Research Related to Measles and Rubella Vaccines and Vaccination, June 2004,” Journal of Infectious Diseases, vol. 192, no. 11, pp. 1890–1897, 2005. View at Publisher · View at Google Scholar · View at PubMed
  214. J. J. Kim, “Mathematical model of HPV provides insight into impacts of risk factors and vaccine,” PLoS Medicine, vol. 3, no. 5, article e164, pp. 587–588, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  215. N. M. Ferguson, D. A. T. Cummings, C. Fraser, J. C. Cajka, P. C. Cooley, and D. S. Burke, “Strategies for mitigating an influenza pandemic,” Nature, vol. 442, no. 7101, pp. 448–452, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  216. L. Temime, D. Guillemot, and P. Y. Boëlle, “Short- and long-term effects of pneumococcal conjugate vaccination of children on penicillin resistance,” Antimicrobial Agents and Chemotherapy, vol. 48, no. 6, pp. 2206–2213, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  217. A. Zvi, N. Ariel, J. Fulkerson, J. C. Sadoff, and A. Shafferman, “Whole genome identification of Mycobacterium tuberculosis vaccine candidates by comprehensive data mining and bioinformatic analyses,” BMC Medical Genomics, vol. 1, p. 18, 2008.
  218. A. Özgür, T. Vu, G. Erkan, and D. R. Radev, “Identifying gene-disease associations using centrality on a literature mined gene-interaction network,” Bioinformatics, vol. 24, no. 13, pp. i277–i285, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  219. A. Ozgur, D. R. Radev, Z. Xiang, and Y. He, “Literature-based discovery of IFN-γ and vaccine-mediated gene interaction networks,” Journal of Biomedicine and Biotechnology, vol. 2010, Article ID 426479, p. 13, 2010. View at Publisher · View at Google Scholar · View at PubMed
  220. H. Streeck, N. Frahm, and B. D. Walker, “The role of IFN-γ Elispot assay in HIV vaccine research,” Nature Protocols, vol. 4, no. 4, pp. 461–469, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  221. H. A. Fletcher, “Correlates of immune protection from tuberculosis,” Current Molecular Medicine, vol. 7, no. 3, pp. 319–325, 2007. View at Publisher · View at Google Scholar · View at Scopus
  222. P. Mansueto, G. Vitale, G. Di Lorenzo, G. B. Rini, S. Mansueto, and E. Cillari, “Immunopathology of leishmaniasis: an update,” International Journal of Immunopathology and Pharmacology, vol. 20, no. 3, pp. 435–445, 2007. View at Scopus
  223. Y. He, R. Vemulapalli, A. Zeytun, and G. G. Schurig, “Induction of specific cytotoxic lymphocytes in mice vaccinated with Brucella abortus RB51,” Infection and Immunity, vol. 69, no. 9, pp. 5502–5508, 2001. View at Publisher · View at Google Scholar · View at Scopus
  224. H. M. Müller, E. E. Kenny, and P. W. Sternberg, “Textpresso: an ontology-based information retrieval and extraction system for biological literature,” PLoS Biology, vol. 2, no. 11, article e309, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  225. B. Yang, S. Sayers, Z. Xiang, and Y. He, “Protegen: a web-based protective antigen database and analysis system,” Nucleic Acids Research, vol. 39, supplement 1, pp. D1073–D1078, 2011. View at Publisher · View at Google Scholar · View at PubMed
  226. H. R. Ansari, D. R. Flower, and G. P. S. Raghava, “AntigenDB: an immunoinformatics database of pathogen antigens,” Nucleic Acids Research, vol. 38, no. 1, Article ID gkp830, pp. D847–D853, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  227. Y. He, Z. Xiang, T. Todd, et al., “Bio-Ontologies 2010: semantic applications in life sciences,” in Proceedings of the 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB '10), p. 4, Boston, Mass, USA, July 2010.
  228. Z. Xiang and Y. He, “Improvement of pubmed literature searching using biomedical ontology,” in Proceedings of the 1st International Conference on Biomedical Ontology (ICBO '09), Buffalo, NY, USA, July 2009.
  229. J. Hur, Z. Xiang, E. Feldman, and Y. He, “Bio-Ontologies 2010: semantic applications in life sciences,” in Proceedings of the 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB '10), Boston, Mass, USA, July 2010.
  230. O. Lund, M. Nielsen, C. Lundergaard, C. Kesmir, and S. Brunak, Immunological Bioinformatics, MIT Press, Cambridge, Mass, USA, 2005.
  231. G. A. Poland, I. G. Ovsyannikova, and R. M. Jacobson, “Personalized vaccines: the emerging field of vaccinomics,” Expert Opinion on Biological Therapy, vol. 8, no. 11, pp. 1659–1667, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  232. M. Sullivan, “Moving candidate vaccines into development from research: lessons from HIV,” Immunology and Cell Biology, vol. 87, no. 5, pp. 366–370, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  233. S. K. Pierce and L. H. Miller, “World Malaria Day 2009: what malaria knows about the immune system that immunologists still do not,” Journal of Immunology, vol. 182, no. 9, pp. 5171–5177, 2009. View at Publisher · View at Google Scholar · View at PubMed
  234. M. Bhasin and G. P. S. Raghava, “A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes,” Journal of Biosciences, vol. 32, no. 1, pp. 31–42, 2007. View at Publisher · View at Google Scholar · View at Scopus
  235. I. A. Doytchinova, P. Guan, and D. R. Flower, “EpiJen: a server for multistep T cell epitope prediction,” BMC Bioinformatics, vol. 7, article 131, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  236. P. A. Reche, H. Zhang, J. P. Glutting, and E. L. Reinherz, “EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology,” Bioinformatics, vol. 21, no. 9, pp. 2140–2141, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  237. A. S. De Groot, M. Ardito, E. M. McClaine, L. Moise, and W. D. Martin, “Immunoinformatic comparison of T-cell epitopes contained in novel swine-origin influenza A (H1N1) virus with epitopes in 2008-2009 conventional influenza vaccine,” Vaccine, vol. 27, no. 42, pp. 5740–5747, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  238. N. Jojic, M. Reyes-Gomez, D. Heckerman, C. Kadie, and O. Schueler-Furman, “Learning MHC I—peptide binding,” Bioinformatics, vol. 22, no. 14, pp. e227–e235, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  239. R. Vita, L. Zarebski, J. A. Greenbaum et al., “The immune epitope database 2.0,” Nucleic Acids Research, vol. 38, no. 1, Article ID gkp1004, pp. D854–D862, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  240. L. Jacob and J. P. Vert, “Efficient peptide-MHC-I binding prediction for alleles with few known binders,” Bioinformatics, vol. 24, no. 3, pp. 358–366, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  241. M. Bhasin and G. P. S. Raghava, “SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence,” Bioinformatics, vol. 20, no. 3, pp. 421–423, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  242. P. Guan, I. A. Doytchinova, C. Zygouri, and D. R. Flower, “MHCPred: bringing a quantitative dimension to the online prediction of MHC binding,” Applied Bioinformatics, vol. 2, no. 1, pp. 63–66, 2003. View at Scopus
  243. G. L. Zhang, A. M. Khan, K. N. Srinivasan, J. T. August, and V. Brusic, “MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides,” Nucleic Acids Research, vol. 33, no. 2, pp. W172–W179, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  244. S. Buus, S. L. Lauemøller, P. Worning et al., “Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach,” Tissue Antigens, vol. 62, no. 5, pp. 378–384, 2003. View at Publisher · View at Google Scholar · View at Scopus
  245. M. Nielsen and O. Lund, “NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction,” BMC Bioinformatics, vol. 10, article 1471, p. 296, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  246. I. Hoof, B. Peters, J. Sidney et al., “NetMHCpan, a method for MHC class i binding prediction beyond humans,” Immunogenetics, vol. 61, no. 1, pp. 1–13, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  247. M. Nielsen, C. Lundegaard, T. Blicher et al., “Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan,” PLoS Computational Biology, vol. 4, no. 7, Article ID e1000107, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  248. P. A. Reche and E. L. Reinherz, “PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands,” Nucleic Acids Research, vol. 33, no. 2, pp. W138–W142, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  249. O. Schueler-Furman, Y. Altuvia, A. Sette, and H. Margalit, “Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles,” Protein Science, vol. 9, no. 9, pp. 1838–1846, 2000. View at Scopus
  250. C. W. Tung and S. Y. Ho, “POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties,” Bioinformatics, vol. 23, no. 8, pp. 942–949, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  251. H. Singh and G. P. S. Raghava, “ProPred1: prediction of promiscuous MHC class-I binding sites,” Bioinformatics, vol. 19, no. 8, pp. 1009–1014, 2003. View at Publisher · View at Google Scholar · View at Scopus
  252. H. Singh and G. P. S. Raghava, “ProPred: prediction of HLA-DR binding sites,” Bioinformatics, vol. 17, no. 12, pp. 1236–1237, 2002. View at Scopus
  253. P. A. Reche, J. P. Glutting, H. Zhang, and E. L. Reinherz, “Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles,” Immunogenetics, vol. 56, no. 6, pp. 405–419, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  254. P. Dönnes and A. Elofsson, “Prediction of MHC class I binding peptides, using SVMHC,” BMC Bioinformatics, vol. 3, article 25, 2002. View at Publisher · View at Google Scholar · View at Scopus
  255. W. Liu, J. Wan, X. Meng, D. R. Flower, and T. Li, “In silico prediction of peptide-MHC binding affinity using SVRMHC,” Methods in Molecular Biology, vol. 409, pp. 283–291, 2007. View at Scopus
  256. H. Bian and J. Hammer, “Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE,” Methods, vol. 34, no. 4, pp. 468–475, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  257. H. U. Chen, NI. Huang, and Z. Sun, “SubLoc: a server/client suite for protein subcellular location based on SOAP,” Bioinformatics, vol. 22, no. 3, pp. 376–377, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  258. M. Bhasin, A. Garg, and G. P. S. Raghava, “PSLpred: prediction of subcellular localization of bacterial proteins,” Bioinformatics, vol. 21, no. 10, pp. 2522–2524, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  259. R. Nair and B. Rost, “Mimicking cellular sorting improves prediction of subcellular localization,” Journal of Molecular Biology, vol. 348, no. 1, pp. 85–100, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  260. O. Emanuelsson, S. Brunak, G. von Heijne, and H. Nielsen, “Locating proteins in the cell using TargetP, SignalP and related tools,” Nature Protocols, vol. 2, no. 4, pp. 953–971, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  261. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool,” Journal of Molecular Biology, vol. 215, no. 3, pp. 403–410, 1990. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  262. F. Chen, A. J. Mackey, C. J. Stoeckert, and D. S. Roos, “OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups,” Nucleic Acids Research, vol. 34, pp. D363–368, 2006. View at Scopus
  263. G. E. Tusnády and I. Simon, “The HMMTOP transmembrane topology prediction server,” Bioinformatics, vol. 17, no. 9, pp. 849–850, 2001. View at Scopus
  264. P. G. Bagos, T. D. Liakopoulos, I. C. Spyropoulos, and S. J. Hamodrakas, “PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins,” Nucleic Acids Research, vol. 32, pp. W400–W404, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  265. N. K. Natt, H. Kaur, and G. P. S. Raghava, “Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods,” Proteins, vol. 56, no. 1, pp. 11–18, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  266. H. R. Bigelow, D. S. Petrey, J. Liu, D. Przybylski, and B. Rost, “Predicting transmembrane beta-barrels in proteomes,” Nucleic Acids Research, vol. 32, no. 8, pp. 2566–2577, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus