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Vladimir Bajic

Named Professor, Applied Mathematics and Computational Science
Director, Computational Bioscience Research Center
Computer, Electrical and Mathematical Science and Engineering Division
Center membership : 
Computational Bioscience


Affiliations

Education Profile

  • ​​​​​​​​​​​​D.Eng.Sc. Electrical Engineering, University of Zagreb, Yugoslavia, 1989
  • M.Sc. Electical Engineering, University of Belgrade, Yugoslavia, 1979
  • Dipl.Eng. Electical Engineering, University of Belgrade, Yugoslavia, 1976

Research Interests

​Professor Bajic is the author of over 300 publications, and more than 100 bioinformatics software products. His primary interest is in facilitating biological discoveries using bioinformatic systems combined with data modeling and machine learning. Emphasis is on inference of new information not explicitly present in biological data, development of systems with such capabilities on HPC and cloud computing systems and their industrial applications. His other interests include synthetic biology simulation models, optimization of metabolic pathways, omics-data analysis, text- & data-mining, cheminformatics and in silico screening for bioactive compounds.

Selected Publications

  • ​FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter level mammalian expression atlas. Nature, 2014; 507(7493):462-70, doi: 10.1038/nature13182
  • Alam I, Antunes A, Kamau AA, Alawi WB, Kalkatawi M, Stingl U & Bajic VB. INDIGO – INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles. Plos One, 2013, 8(12). doi: 10.1371/journal.pone.0082210
  • Schaefer U, Schmeier S & Bajic VB. TcoF-DB: dragon database for human transcription co-factors and transcription factor interacting proteins. Nucleic Acids Research, 2011, 39(Database issue):D106-10. doi: 10.1093/nar/gkq945, http://en.wikipedia.org/wiki/TcoF-DB
  • [‡ contributed equally] Ravasi T‡, Suzuki H‡, Cannistraci CV‡, Katayama S‡, Bajic VB‡, et al (Mar 2010) An atlas of combinatorial transcriptional regulation in mouse and man, Cell, 140(5), 744-752. doi: 10.1016/j.cell.2010.01.044.
  • Suzuki H et al., The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line, Nature Genetics, 2009, 41(5):553-62. doi: 10.1038/Ng.375
  • Carninci P et al., Genome-wide analysis of mammalian promoter architecture and evolution. Nature Genetics, 2006, 38:626-635, doi: 10.1038/ng1789
  • Carninci P et al., FANTOM Consortium; RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project Core Group). The Transcriptional Landscape of the Mammalian Genome, Science, 2005; 309:1559-1563, doi 10.1126/Science.1112014
  • Tiffin N et al., Integration of Text- and Data-Mining Using Ontologies Successfully Selects Disease Gene Candidates, Nucleic Acids Research, 2005; 33(5):1544-1552, doi: 10.1093/nar/gki296
  • Bajic VB, Tan SL, Suzuki Y & Sugano S. Promoter prediction analysis on the whole human genome, Nature Biotechnology, 2004; 22(11):1467-73, doi: 10.1038/nbt1032
  • Bajic VB et al., Dragon ERE Finder ver.2: A Tool for Accurate Detection and Analysis of Estrogen Response Elements in Vertebrate Genomes, Nucleic Acids Research, 2003; 31(13):3605-3607, doi: 10.1093/Nar/Gkg517