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Vladimir Brusic Publications

    JOURNAL ARTICLES      BOOKS AND BOOK CHAPTERS       REFEREED CONFERENCE ARTICLES       OTHER ARTICLES
    Journal Articles
  1. Tong J.C., Zhang G.L., Tan T.W., August J.T., Brusic V. and Ranganathan S. (2006). Prediction of HLA-DQ3.2 ligands: evidence of multiple registers in class II binding peptides. Bioinformatics (in press).
  2. Brusic V. (2006). Computational models in molecular medicine and bioinformatics - the use and prospects. Magyar Tudomany (Hungarian Science) - an official journal of the Hungarian Academy of Sciences (invited paper) (in press).
  3. Gupta V., Tabiin T.M., Sun K., Chandrasekaran A., Anwar A., Yang K., Chikhlikar P., Salmon J., Brusic V., Marques E.T., Kellathur S.N. and August T.J. (2006). SARS coronavirus nucleocapsid immunodominant T-cell epitope cluster is common to both exogenous recombinant and endogenous DNA-encoded immunogens. Virology 347, 127-139.
  4. Brusic V. (2006). Information management for the study of allergies. Inflammation & Allergy - Drug Targets, 5(1), 35-42.
  5. Tan P.T.J., Veeramani A., Srinivasan K.N., Ranganathan S., Brusic V. (2006). Scorpion 2: a database for structure-function analysis of scorpion toxins. Toxicon 47(3), 356-363.
  6. Tan P.T.J., Ranganathan S. and Brusic V. (2006). Deduction of functional peptide motifs in scorpion toxins. Journal of Peptide Science (in press).
  7. Tongchusak S., Chaiyaroj S.C., Veeramani A., Koh J.L.Y., Brusic V. (2005). CandiVF - Candida albicans virulence factor database. International Journal of Peptide Research and Therapeutics 11(4), 271-277.
  8. Zhang G., Khan A.M., Srinivasan K.N., August T.J. and Brusic V. (2005). Neural models for predicting viral vaccine targets. Journal of Bioinformatics and Computational Biology 3, 1207-1225.
  9. Carninci P., Kasukawa T., Katayama S., Gough J., Frith M.C., Maeda N., Oyama R., Ravasi T., Lenhard B., Wells C., Kodzius R., Shimokawa K., Bajic V.B., Brenner S.E., Batalov S., Forrest A.R.R., Zavolan M., Davis M.J., Wilming L.G., Aidinis V., J. Allen J.E., Ambesi-Impiombato A., Apweiler R., Aturaliya R.N., Bailey T.L., Bansal M., Baxter L., Beisel K.W., Bersano T., Bono H., Chalk A.M., Chiu K.P., Choudhary V., Christoffels A., Clutterbuck D.R., Crowe M.L., Dalla E., Dalrymple B.P., de Bono B., Della Gatta G., di Bernardo D., Down T., Engstrom E., Fagiolini M., Faulkner G., Fletcher C.F., Fukushima T., Furuno M., Futaki S., Gariboldi M., Georgii-Hemming P., Gingeras T. R., Gojobori T., Green R. E., Gustincich S., Harbers M., Hayashi Y., Hensch T.K., Hirokawa N., Hill D., Huminiecki L., Iacono M., Ikeo K., Iwama A., Ishikawa T., Jakt M., Kanapin A., Katoh M., Kawasawa Y., Kelso J., Kitamura H., Kitano H., Kollias G., Krishnan S.P.T., Kruger A., Kummerfeld S.K., Kurochkin I.V., Lareau L.F., Lazarevic D., Lipovich L., Liu J., Liuni S., McWilliam S., Madan Babu M., Madera M., Marchionni L., Matsuda H., Matsuzawa S., Miki H., Mignone F., Miyake S., Morris K., Mottagui-Tabar S., Mulder N., Nakano N., Nakauchi H., Ng P., Nilsson R., Nishiguchi S., Nishikawa S., Nori F., Ohara O., Okazaki Y., Orlando V., Pang K.C., Pavan W.J., Pavesi G., Pesole G., Petrovsky N., Piazza S., Reed J., Reid J.F., Ring B.Z., Ringwald M., Rost B., Ruan Y., Salzberg S.L., Sandelin A., Schneider C., Schonbach C., Sekiguchi K., Semple C.A.M., Seno S., Sessa L., Sheng Y., Shibata Y., Shimada H., Shimada K., Silva D., Sinclair B., Sperling S., Stupka E., Sugiura K., Sultana R., Takenaka Y., Taki K., Tammoja K., Tan S.L., Tang S., Taylor M.S., Tegner J., Teichmann S.A., Ueda H.R., van Nimwegen E., Verardo R., Wei C.L., Yagi K., Yamanishi H., Zabarovsky E., Zhu S., Zimmer A., Hide W., Bult C., Grimmond S.M., Teasdale R.D., Liu E.T., Brusic V., Quackenbush J., Wahlestedt C., Mattick J.S., Hume D.A., RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project Core Group), Kai C., Sasaki D., Tomaru Y., Fukuda S., Kanamori-Katayama M., Suzuki M., Aoki J., Arakawa T., Iida J., Imamura K., Itoh M., Kato T., Kawaji H., Kawagashira N., Kawashima T., Kojima M., Kondo S., Konno H., Nakano K., Ninomiya N., Nishio T., Okada M., Plessy C., Shibata K., Shiraki T., Suzuki S., Tagami M., Waki K., Watahiki A., Okamura-Oho Y., Suzuki H., Kawai J. and Hayashizaki Y. (2005). The transcriptional landscape of the mammalian genome. Science 309, 1559-1563.
  10. Schonbach C., Koh J.L.Y., Flower D.R. and Brusic V. (2005). An update on functional molecular immunology database FIMM. Applied Bioinformatics 4, 25-31.
  11. Brusic V., August T.J. and Petrovsky N. (2005). Information technologies for vaccine research. Expert Review of Vaccines 4, 407-417.
  12. Brusic V. and Petrovsky N. (2005). Immunoinformatics and its relevance to understanding human immune disease. Expert Review of Clinical Immunology 1(1), 145-157.
  13. Zhang G.L., Khan A.M., Srinivasan K.N., August J.T. and Brusic V. (2005). MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Research 33, W172-179.
  14. Zhang G., Srinivasan K.N., Veeramani A., August T.J. and Brusic V. (2005) PREDBALB/c: a system for prediction of peptide binding to the H2d molecules, a haplotype of the BALB/c mouse. Nucleic Acids Research 33, W180-183.
  15. Tan P.T.J., Srinivasan K.N., Seah S.H., Koh J.L.Y., Tan T.W., Ranganathan S. and Brusic V. (2005). Accurate prediction of scorpion toxin functional properties from primary sequences. Journal of Molecular Graphics and Modelling 24, 17-24.
  16. Mandic M., Castelli F., Janjic B., Almunia C., Andrade P., Gillet D., Brusic V., Kirkwood J.M., Maillere B. and Zarour H.M. One NY-ESO-1-derived epitope that promiscuously binds to multiple HLA-DR and HLA-DP4 molecules and stimulates autologous CD4+ T Cells from patients with NY-ESO-1-expressing melanoma. Journal of Immunology 174(3), 1751-1759 (2005).
  17. Srinivasan K.N., Brusic V. and August J.T. (2004). New technologies for vaccine development. Drug Development Research 62, 393-392.
  18. Siew J.P., Khan A.M., Tan P.T., Koh J.L., Seah S.H., Koo C.Y., Chai S.C., Armugam A., Brusic V. and Jeyaseelan K. (2004). Systematic analysis of snake neurotoxins functional classification using a data warehousing approach. Bioinformatics 20, 3466-3480.
  19. Brusic V. and August J.T. (2004). The changing field of vaccine development in the genomics era. Pharmacogenomics 5(6), 597-600.
  20. Flynn J.C., McCormick D.J., Brusic V., Wan Q., Panos J.C., Giraldo A.A., David C.S. and Kong Y.C. (2004). Pathogenic human thyroglobulin peptides in HLA-DR3 transgenic mouse model of autoimmune thyroiditis. Cellular Immunology 229, 79-85.
  21. Brusic V., Bajic V.B. and Petrovsky N. (2004). Computational methods for prediction of T-cell epitopes - a framework for modeling, testing, and applications. Methods 34, 436-443.
  22. Petrovsky N. and Brusic V. (2004). Virtual models of the HLA class I antigen processing pathway. Methods 34, 429-435.
  23. Srinivasan K.N., Zhang G., Khan A.M., August T.J. and Brusic V. (2004). Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens. Bioinformatics 20 Suppl 1, I297-I302.
  24. Brusic V. (2004). Computational models for molecular medicine. Chemical Industry 58 (6a), 79-81.
  25. Lenffer J., Lai P., El Mejaber W., Khan A.M., Koh J.L.Y., Tan P.T.J., Seah S.H. and Brusic V. (2004). CysView: Protein classification based on cysteine pairing patterns. Nucleic Acids Research 32, W350-355.
  26. Silva D.G., Schonbach C., Brusic V., Socha L., Nagashima T. and Petrovsky N. Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system. (2004). BMC Genomics 5, 28.
  27. Brusic V., Takagi Y. and Nakamura H. (2004). Bioinformatics in Asia (in Japanese). Tanpakushitsu kakusan koso (Protein, nucleic acid, enzyme) 49, 74-83.
  28. Brusic V. and Flower D.R. (2004). Bioinformatics tools for identifying T-cell epitopes. Drug Discovery Today: Biosilico 2, 18-23.
  29. Bramachary M., Krishnan S.P.T., Koh J.L.Y., Seah SH., Tan T.W., Brusic V., Bajic V.B. (2004). ANTIMIC: a database of antimicrobial sequences. Nucleic Acids Research 32, D586-589.
  30. Bajic V.B. and Brusic V. (2003). Computational detection of vertebrate RNA polymerase II promoters. Methods in Enzymology 370, 237-250.
  31. Petrovsky N., Schonbach C. and Brusic V. (2003). Bioinformatics strategies for better understanding of immune function. In Silico Biology 3, 0034.
  32. Fry B.G. Wuster W., Kini R.M., Brusic V., Khan A., Venkataraman D. and Rooney A.P. (2003). Molecular evolution and phylogeny of snake venom three finger toxins. Journal of Molecular Evolution 57, 110-129.
  33. Brusic V., Pillai R.S., Silva D.G. and Petrovsky N., RIKEN GER Group Members, and Schonbach C. (2003). Cytokine-related genes identified from the RIKEN full-length mouse cDNA dataset. Genome Research 13, 1307-1317.
  34. Brusic V. and Petrovsky N. (2003). Bioinformatics for Characterisation of Allergens, Allergenicity and Allergic Cross-Reactivity. Trends in Immunology 24, 225-228.
  35. Brusic V. (2003). From immunoinformatics to immunomics. Journal of Bioinformatics and Computational Biology 1, 179-181.
  36. Tan P.T.J, Khan A.M. and Brusic V. (2003). Bioinformatics for venom and toxin sciences. Briefings in Bioinformatics 4, 53-62.
  37. Brusic V., Millot M., Petrovsky N., Gendel S.M., Gigonzac O. and Stelman S.J. (2003). Allergen databases. Allergy 58, 1093-1100.
  38. Brusic V., Petrovsky N., Gendel S.M., Millot M., Gigonzac O. and Stelman S.J. (2003). Computational Tools for the Study of Allergens. Allergy 58, 1083-1092.
  39. Tatsumi T., Kierstead L.S., Ranieri E., Gesualdo L., Schena F.P., Finke J.H., Bukowski R.M., Brusic V., Sidney J., Sette A., Logan T.F., Kasamon Y.L., Slingluff C.L.Jr., Kirkwood J.M., Storkus W.J. (2003). MAGE-6 Encodes HLA-DRbeta1*0401-presented Epitopes Recognized by CD4+ T Cells from Patients with Melanoma or Renal Cell Carcinoma. Clinical Cancer Research 9, 947-954.
  40. Bajic V.B., Seah S.H., Chong A., Krishnan S.P.T., Koh J.L.Y. and Brusic V. (2003). Computer model for recognition of transcription start sites in RNA polimerase II promoters of vertebrates. Journal of Molecular Graphics and Modelling 21, 323-332.
  41. Bajic V.B., Tang S., Han H., Brusic V. and Hatzigeorgiou A.G. (2002). Artificial neural networks based systems for recognition of genomic signals and regions: a review. Informatica 26, 389-400.
  42. Okazaki Y., Furuno M., Kasukawa T., Adachi J., Bono H., Kondo S., Nikaido I., Osato N., Saito R., Suzuki H., Yamanaka I., Kiyosawa H., Yagi K., Tomaru Y., Hasegawa Y., Nogami A., Schonbach C., Gojobori T., Baldarelli R., Hill D.P., Bult C., Hume D.A., Quackenbush J., Schriml L.M., Kanapin A., Matsuda H., Batalov S., Beisel K.W., Blake J.A., Bradt D., Brusic V., Chothia C., Corbani L.E., Cousins S., Dalla E., Dragani T.A., Fletcher C.F., Forrest A., Frazer K.S., Gaasterland T., Gariboldi M., Gissi C., Godzik A., Gough J., Grimmond S., Gustincich S., Hirokawa N., Jackson I.J., Jarvis E.D., Kanai A., Kawaji H., Kawasawa Y., Kedzierski R.M., King B.L., Konagaya A., Kurochkin I.V., Lee Y., Lenhard B., Lyons P.A., Maglott D.R., Maltais L., Marchionni L., McKenzie L., Miki H., Nagashima T., Numata K., Okido T., Pavan W.J., Pertea G., Pesole G., Petrovsky N., Pillai R., Pontius J.U., Qi D., Ramachandran S., Ravasi T., Reed J.C., Reed D.J., Reid J., Ring B.Z., Ringwald M., Sandelin A., Schneider C., Semple C.A., Setou M., Shimada K., Sultana R., Takenaka Y., Taylor M.S., Teasdale R.D., Tomita M., Verardo R., Wagner L., Wahlestedt C., Wang Y., Watanabe Y., Wells C., Wilming L.G., Wynshaw-Boris A., Yanagisawa M, Yang I, Yang L, Yuan Z, Zavolan M, Zhu Y, Zimmer A, Carninci P., Hayatsu N., Hirozane-Kishikawa T., Konno H., Nakamura M., Sakazume N., Sato K., Shiraki T., Waki K., Kawai J., Aizawa K., Arakawa T., Fukuda S., Hara A., Hashizume W., Imotani K., Ishii Y., Itoh M., Kagawa I., Miyazaki A., Sakai K., Sasaki D., Shibata K., Shinagawa A., Yasunishi A., Yoshino M., Waterston R., Lander E.S., Rogers J., Birney E. and Hayashizaki Y. (2002). Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420, 563-573.
  43. Noguchi H., Kato R., Hanai T., Matsubara Y., Honda H., Brusic V. and Kobayashi T. (2002). Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. Journal of Bioscience and Bioengineering 94, 264-270.
  44. Petrovsky N. and Brusic V. (2002). Computational immunology: The coming of age. Immunology and Cell Biology 80, 248-254.
  45. Brusic V., Petrovsky N., Zhang G. and Bajic V.B. (2002). Prediction of promiscuous peptides that bind HLA class I molecules. Immunology and Cell Biology 80, 280-285.
  46. Schonbach C., Yu K. and Brusic V. (2002). Large-scale computational identification of HIV T-cell epitopes. Immunology and Cell Biology 80, 300-306.
  47. Yu K., Petrovsky N., Schonbach C., Koh J.L.Y. and Brusic V. (2002). Methods for prediction of peptide binding to MHC molecules: a comparative study. Molecular Medicine 8, 137-148.
  48. Zarour H.M., Maillere B., Brusic V., Coval K., Williams E., Pouvelle-Moratille S., Castelli F., Land S., Bennouna J., Logan T. and Kirkwood J.M. (2002). NY-ESO-1 119-143 is a promiscuous MHC class II T-helper epitope recognized by Th1 and Th2-type tumor-reactive CD4+ T cells. Cancer Research 62, 213-218.
  49. Bajic V.B., Chong A., Seah S.H. and Brusic V. (2002). Intelligent system for vertebrate promoter recognition. IEEE Intelligent Systems 17, 64-70.
  50. Schonbach C., Koh J.L.Y., Flower D.R., Wong L. and Brusic V. (2002). FIMM, a database of functional molecular immunology - update 2001. Nucleic Acids Research 30, 226-229.
  51. Bajic V.B., Seah S.H., Chong A., Zhang G., Koh J.L.Y. and Brusic V. (2002). Dragon Promoter Finder: recognition of vertebrate RNA Polymerase II promoters. Bioinformatics 18, 198-199.
  52. Srinivasan K.N., Gopalakrishnakone P., Tan P.T., Chew K.C., Cheng B., Kini R.M., Koh J.L.Y, Seah S.H. and Brusic V. (2002). SCORPION, a molecular database of scorpion toxins. Toxicon 40, 23-31.
  53. Petrovsky N., Tam S.K., Brusic V., Russ G., Socha L. and Bajic V.B. (2002). Use of artificial neural networks in improving renal transplantation outcomes. Graft 4, 6-13.
  54. Kierstad L.S., Ranieri E., Olson W., Brusic V., Singluff C.L.Jr., Kirkwood J.M. and Storkus W.J. (2001). GP100/MEL17 and tyrosinase encode multiple epitopes recognized by Th1-type CD4+ T cells. British Journal of Cancer 85, 1735-1745.
  55. Yan Y., Panos J.C., McCormick D.J., Wan Q., Giraldo A.A., Brusic V., David C.S. and Kong Y.M. (2001). Characterization of a novel H2A-E+ transgenic model susceptible to heterologous but not self tyroglobulin in autoimmune thyroiditis: thyroiditis transfer with V\3378+ T cells. Cellular Immunology 212, 63-70.
  56. Brusic V., Bucci K., Schonbach C., Petrovsky N., Zeleznikow J. and Kazura J.W. (2001). Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. Journal of Molecular Graphics and Modelling 19, 405-411.
  57. Zarour H., Storkus W.J., Brusic V., Williams E., Old L.J. and Kirkwood J.M. (2000). NY-ESO-1 encodes DRB1*0401-restricted epitopes recognized by melanoma-reactive CD4+ T cells. Cancer Research 60, 4946-4952.
  58. Schonbach C., Kowalski-Saunders P. and Brusic V. (2000). Data warehousing in molecular biology. Briefings in Bioinformatics 1, 190-198.
  59. Brusic V., Zeleznikow J. and Petrovsky N. (2000). Molecular immunology databases and data repositories. Journal of Immunological Methods 238, 17-28.
  60. Zarour H.M., Kirkwood J.M., Salvucci-Kierstead L., Herr W., Brusic V., Singluff C.L.Jr, Sette A. and Storkus W.J. (2000). Melan-A/MART-151-73 represents an immunogenic HLA-DR4-restricted epitope recognized by melanoma-reactive CD4+ T cells. Proceedings of the National Academy of Sciences USA 97, 400-405.
  61. Schonbach C., Koh J.L.Y., Sheng X., Wong L. and Brusic V. (2000). FIMM, a database of functional molecular immunology. Nucleic Acids Research 28, 222-224.
  62. Brusic V. and Zeleznikow J. (1999). Knowledge Discovery and Data Mining in Biological Databases. Knowledge Engineering Review 14, 257-277.
  63. Brusic V. and Zeleznikow J. (1999). Computational binding assays of antigenic peptides. Letters in Peptide Science 6, 313-324.
  64. Brusic V., van Endert P., Zeleznikow J., Daniel S., Hammer J. and Petrovsky N. (1999). A neural network model approach to the study of human TAP transporter. In Silico Biology 1, 109-121.
  65. Honeyman M.C., Brusic V., Stone N. and Harrison L.C. (1998). Neural network-based prediction of candidate T-cell epitopes. Nature Biotechnology 16, 966-969.
  66. Daniel S., Brusic V., Caillat-Zucman S., Petrovsky N., Harrison L., Riganelli D., Sinigaglia F., Gallazzi F., Hammer J. and van Endert P.M. (1998). Relationship between peptide selectivities of human transporters associated with antigen processing and HLA class I molecules. Journal of Immunology 161, 617-624.
  67. Brusic V., Rudy G. and Harrison L.C. (1998). MHCPEP - a database of MHC-binding peptides: update 1997. Nucleic Acids Research 26, 368-371.
  68. Brusic V., Rudy G., Honeyman M.C., Hammer J. and Harrison L.C. (1998). Prediction of MHC class-II binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14, 121-130.
  69. Honeyman M.C., Brusic V. and Harrison L.C. (1997). Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes (IDDM). Annals of Medicine 29, 401-404.
  70. Ramakrishna V., Negri D.R.M., Brusic V., Fontanelli R., Canevari S., Bolis G., Castelli C. and Parmiani G. (1997). Generation and phenotypic characterisation of new human ovarian cancer lines with the identification of antigens potentially recognizable by HLA-restricted cytotoxic T cells. International Journal of Cancer 73, 143-150.
  71. Harrison L.C., Honeyman M.C., Tremblau S., Gregori S., Gallazzi F., Augstein P., Brusic V., Hammer J. and Adorini L. (1997). Peptide binding motif for I-Ag7, the class II MHC molecule of NOD and Biozzi AB/H mice. Journal of Experimental Medicine 185, 1013-1021.
  72. Brusic V., Rudy G., Kyne A.P. and Harrison L.C. (1997). MHCPEP, a database of MHC-binding peptides: update 1996. Nucleic Acids Research 25, 269-271.
  73. Nakagawa K., Brusic V., McColl G. and Harrison LC. (1997). Evidence for differential expression of endogenous retroviruses in the synovial compartment in rheumatoid arthritis. Arthritis and Rheumatism 40, 627-638.
  74. Brusic V., Rudy G., Kyne A.P. and Harrison L.C. (1996). MHCPEP - a database of MHC-binding peptides: update 1995. Nucleic Acids Research 24, 242-244.
  75. Rudy G., Stone N., Harrison L.C., Colman P.G., McNair P., Brusic V., French M.B., Honeyman M.C., Tait B. and Lew A.M. (1995). Similar peptides from two beta cell autoantigens, proinsulin and glutamic acid decarboxylase, stimulate T cells of individuals at risk for insulin-dependent diabetes. Molecular Medicine 1, 625-633.
  76. Brusic V., Rudy G. and Harrison L.C. (1995). Prediction of MHC binding peptides using artificial neural networks. Complexity International 2, 1995, http://www.csu.edu.au/ci/vol2/vbb/vbb.html
  77. Brusic V., Rudy G. and Harrison L.C. (1994). MHCPEP - a database of MHC binding peptides. Nucleic Acids Research 22, 3663-3665.
  78. Brusic V., Bozovic Z., Stojiljkovic Z., Djakovic D. and Trajkovic M. (1990). Clinical information systems planning in Belgrade health care region. Annual Review in Automatic Programming 14(II), 27-29.
  79. Nedeljkovic P., Trajkovic M. and Brusic V. (1988). BELGER - a program package for analysis of independence of the aged persons (In Serbian). Tehnika, special issue Informatics and Productivity (Journal of the Yugoslav Society of Engineers and Technicians), pp. 219-224, Belgrade.


    80. Books and Book Chapters     top of page
  80. Brusic V. and Khan A.M. (editors) (2005). Abstract Book. The 3rd Asia-Pacific Bioinformatics Conference & Singapore Bioinformatics Week. World Scientific, January 2005.
  81. Koh J.L.Y. and Brusic V. (2005). Bioinformatics Database Warehousing. In Chen YPP (editor), Bioinformatics Technology, pages 45-62, Springer.
  82. Akutsu T., Brusic V., Kanehisa M., Miyano S., Takagi T. (editors). Genome Informatics 2004. Genome Informatics Series Vol. 15. No. 2. Universal Academy Press, Inc, Tokyo, Japan 2004.
  83. Brusic V. and Koh J.L.Y. (2004). Genetic databases. In Ruvinsky A. and Graves J. Mammalian Genomics, pages 411-427. CAB International, Wallingford, Oxon, UK.
  84. Motta S. and Brusic V. (2004). Mathematical Modelling of the Immune System. In G. Ciobanu, G. Rozenberg (eds.) Modelling in Molecular Biology, pages 193-218, Natural Computing Series, Springer, 2004.
  85. Brusic V. and Petrovsky N. (2003). Immunoinformatics - the new kid in town. In Bock G. and Goode J. (eds). In Immunoinformatics: bioinformatics strategies for better understanding of immune function, Novartis Foundation Symposium 254, pp 3-13.
  86. Petrovsky N., Silva D. and Brusic V. (2003). The future for computational modelling and prediction systems in clinical immunology. In Immunoinformatics: bioinformatics strategies for better understanding of immune function, Novartis Foundation Symposium 254, pp 23-32.
  87. Brusic V. and Petrovsky N. (2002). Bioinformatic analysis for assessing allergenic safety of proteins. Life Sciences Technology pp. 73-76. WMRC Business Briefings Series. London UK.
  88. Brusic V. and Harrison LC. (1998). Computational methods in prediction of MHC-binding peptides. In Michalewicz M. (ed), Advances in Computational Life Sciences: Humans to Proteins, pp. 213-222, CSIRO Publishing, Melbourne.
  89. Brusic V., Rudy G. and Harrison L.C. (1994). Prediction of MHC binding peptides using artificial neural networks. In Stonier R.J. and Yu X.S. (eds), Complex Systems: Mechanism of Adaptation, pp. 253-260, IOS Press, Amsterdam/OHMSHA Tokyo.
  90. Brusic V., Bozovic Z., Stojiljkovic Z., Djakovic D. and Trajkovic M. (1990). Clinical information systems planning in Belgrade health care region. Milovanovic R. and Elzer P. (eds). Experience with the Management of Software Projects, pp. 27-29, Pergamon, Oxford UK, 1990.


    91. Conference Proceedings Articles - fully refereed     top of page
  91. Lam K.T., Koh J.L.Y., Veeravalli B. and Brusic V. (2006). Incremental maintenance of biological databases using association rule mining. Lecture Notes in Bioinformatics (in press).
  92. Handoko S.D., Kwoh C.K., Ong Y.S., Zhang G.L. and Brusic V. (2006). Extreme Learning Machine for Predicting HLA-Peptide Binding. Lecture Notes in Computer Science (in press).
  93. Rajapakse M., Schmidt B. and Brusic V. (2006). Multi-objective evolutionary algorithm for discovering peptide binding motifs. Lecture Notes in Computer Science 3907, 1 49-158.
  94. Miotto O, Tan TW and Brusic V. (2005). Supporting the curation of biological databases with reusable text mining. Genome Informatics, 16(2), 32-44.
  95. Miotto O., Tan T.W. and Brusic V. (2005). Extraction by example: induction of structural rules for the analysis of molecular sequence data from heterogeneous sources. Lecture Notes in Computer Science 3578, 398-405.
  96. Rajapakse M., Wyse L., Schmidt B. and Brusic V. (2005). Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm. Lecture Notes in Computer Science 3578, 440-447.
  97. Bozic I., Zhang G.L., Brusic V. (2005). Predictive vaccinology: optimisation of predictions using support vector machine classifiers. Lecture Notes in Computer Science 3578, 375-381.
  98. Koh J.L.Y. and Brusic V. (2004). Warehousing of Biological Data. International Workshop on Knowledge Discovery in BioMedicine (KDbM-04). A PRICAI 2004 Workshop, August 2004
  99. Koh J.L.Y., Lee M.L., Khan A.M., Tan P.T.J and Brusic V. (2004). Duplicate Detection in Biological Data using Association Rule Mining. 2nd European Workshop on Data Mining and Text Mining for Bioinformatics. An ECML/PKDD 2004 workshop, Pisa, Italy, September 24, 2004.
  100. Koh J.L.Y., Krishnan S.P.T., Seah S.H., Tan P.T., Khan A., Lee M.L. and Brusic V. (2004). BioWare: A framework for bioinformatics data retrieval, annotation and publishing. Search and Discovery in Bioinformatics. A SIGIR 2004 Workshop, 27th Annual International ACM SIGIR Conference on Research and Development in IR. July 29, 2004, Sheffield, UK.
  101. Brusic V. and Petrovsky N. (2003). Immunoinformatics - the new kid in town. Novartis Foundation Symposium Series 254, 3-13.
  102. Petrovsky N., Silva D.G. and Brusic V. (2003). The future for computational modeling and prediction systems in clinical immunology. Novartis Foundation Symposium Series 254, 23-32.
  103. Bajic V., Brusic V., Li J., Ng S.K. and Wong L. (2003). From Informatics to Bioinformatics. Proceedings of the 1st Asia Pacific Bioinformatics Conference, Adelaide, Australia, February 2003.
  104. Bajic V.B., Tang S, H. Han H. and Brusic V. (2002). Recognition of genomic signals and regions by artificial neural networks: an overview. Proceedings of the 5th International Multi-conference Information Society 2002, Ljubljana, Slovenia, 14-18 October, 2002.
  105. Brusic V. and Zeleznikow J. (1999). Artificial neural network applications in immunology. Proceedings of the 1999 International Joint Conference on Neural Networks IJCNN'99. Manuscript #2034 Session: 10.5A.
  106. Brusic V., Zeleznikow J., Sturniolo T., Bono E. and Hammer J. (1999). Data cleansing for computer models: a case study from immunology. Proceedings of ICONIP99 Sixth International Conference on Neural Information Processing, IEEE, 603-609.
  107. Hon L., Abernethy N.F., Brusic V., Chai J. and Altman R. (1998). HCWeb: Converting a WWW database into a knowledge-based collaborative environment. Proceedings of AMIA Symposium, 947-951.
  108. Brusic V., Schonbach C., Takiguchi M., Ciesielski V. and Harrison L.C. (1997). Usi Application of genetic search in derivation of matrix models of peptide binding to MHC molecules. ISMB 5:75-83.
  109. Brusic V., Rudy G. and Ellis G. (1994). Using conceptual graphs to model the immune system: specification of MHC/peptide binding prediction. Proceedings of the 1st Australian Conceptual Structures Workshop, pp. 78-91, Armidale.
  110. Tomovic R., Bozovic Z., Brusic V., Djakovic D., Srdanovic V., Stojiljkovic Z. and Trajkovic M. (1988). Activities of the Belgrade University Center for multidisciplinary Studies in medical software design during 1985-1987. (In Serbian). Proceedings of the XXII Yugoslav Conference of ETAN, Sarajevo.
  111. Brusic V., Drouin G. (1987). Hip and knee moments as control mechanisms for an above-knee prosthetic leg. Proceedings of the 2nd International Symposium Automaton and Robot, pp. 94-104, Belgrade.


    112. Other Publications     top of page
  112. Basford K, DeLacy I, Brusic V. (2005). Plant breeding informatics. Proceedings of the 10th International Congress of SABRAO "How to Utilize Crop Diversity for Productivity and Sustainability, Breeding Science and Technology for the New Era", Tsukuba, Japan, Aug. 22-23, 2005. (Invited paper)
  113. Zhang G, Khan AM, Srinivasan KN, August JT, Brusic V. (2003). Neural Models for Predicting Viral Vaccine Targets. Proceedings of the NCEI'03: Neurocomputing and Evolving Intelligence 2003, Auckland, New Zealand, November 20-21, 2003.
  114. Silva D.G., Schonbach C., Brusic V., Socha L.A., Nagashima T., RIKEN GER Group Members and Petrovsky N. (2003). Identification of novel "pathologs" (human disease-related gene candidates) from the RIKEN full-length mouse cDNA dataset. Genome Research, 13, 1559.
  115. Ranieri E., Tatsumi T., Kierstead L.S., Finke J.H., Bukowski R.M., Brusic V., Sidney J., Sette A., Di Natale C., Cellie M., Kwok W., Grandaliano G., Schena F.P., Gesualdo L., Storkus W.J. (2002). Identification of HLA-DRB1*0401+-restricted MAGE-6 derived epitopes recognized by Th1/Th2 CD4+T-helper cells in renal cell carcinoma (RCC) patients (PTS). Journal of the American Society of Nephrology. 13, 505A-505A Suppl. S SEP 2002.
  116. Hanai T., Noguchi H., Matsubara Y., Takeda K., Honda H., Brusic V. and Kobayashi T. (2000). Computational design of proteinous drug employing hidden Markov model. Genome Informatics 11, 394-395.
  117. Petrovsky N. and Brusic V. (2000). Bioinformatics in life sciences research. Today's Life Sciences (Australia), November/December 2000, 26-28 .
  118. Kierstad L.S., Rice C., Brusic V. and Storkus W.J. (1998). DC facilitate the generation of tumor antigen-specific CD4+ T cells with diverse specificities. (Meeting abstract). Journal of Leukocyte Biology F32, Suppl. 2.
  119. Brusic V., Wilkins J.S, Stanyon C.A. and Zeleznikow J. (1998). Data learning: understanding biological data. In Merrill G. and Pathak D.K. (eds.) Knowledge Sharing Across Biological and Medical Knowledge Based Systems: Papers from the 1998 AAAI Workshop pp. 12-19. AAAI Technical Report WS-98-04. AAAI Press.
  120. Hon L., Abernethy N.F., Brusic V., Chai J. and Altman R. (1998). HCWeb: Converting a WWW database into a knowledge-based collaborative environment. Section on Medical Informatics Technical Report SMI-98-0724, Stanford University.
  121. Daniel S., Brusic V., Caillat-Zucman S., Petrovsky N., Harrison L., Riganelli D., F. Sinigaglia F., Gallazzi F., Hammer J. and van Endert P.M. (1997). The relationship between peptide selectivity of HLA class I molecules and TAP transporters. Immunology Letters 56(1-3):240. (Meeting abstract).
  122. Brusic V., Rudy G. and Harrison L.C. (1997). Molecular mimicry - from hypothesis towards evidence. Immunology Today 18(2):95-96. (Letter)
  123. Baum H., Brusic V., Choudhuri K., Cunningham P., Vergani D. and Peakman M. (1995). MHC molecular mimicry in diabetes. Nature Medicine 1(5):388. (Letter).
  124. Rudy G., Brusic V., Harrison L.C. and Lew A.M. (1995). Sequence similarity between the b-cell autoantigens, proinsulin and glutamic acid decarboxylase: a clue to pathogenesis? Immunology Today 16(8):406-407. (Letter).

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Last updated: 20-Apr-2006