Vladimir Bajic

Professor, Applied Mathematics and Computational Science
Director, Computational Bioscience Research Center

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 400 research publications, 100+ bioinformatics and machine learning software products, and nine patents. His primary interest is in facilitating biomedical discoveries using computational systems combined with data modeling and artificial intelligence (AI). Emphasis is on inference of new information not explicitly present in biomedical data, development of systems with such capabilities and their industrial applications. Current research focus: AI & health informatics; biomedical knowledge-, text- & data-mining; AI/machine learning modeling; drug repositioning; diagnostic, screening & prognostic biomarkers; information integration.  

Selected Publications

  • Olayan RS, Ashoor H, Bajic VB. DDR: Efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics. 2018 Apr 1;34(7):1164-1173. doi: 10.1093/bioinformatics/btx731 
  • Bin Raies A, Bajic VB. In silico toxicology: computational methods for prediction of chemical toxicity. WIRES Computational Molecular Science. 2016 Mar;6(2):147-172. Doi: 10.1002/wcms.1240
  • 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
  • [‡ 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