Genomics
  • Wigle, D., Jurisica, I., N. Radulovich, M. Pintilie, J. Rossant, N. Liu, C. Lu, J. Woodgett, I. Seiden, M. Johnston, S. Keshavjee, G. Darling, T. Winton, B. Breitkreutz, P. Jorgenson, M. Tyers, F. A. Shepherd, M.S. Tsao. Molecular profiling of non-small cell lung cancer and correlation with disease-free survival Cancer Research, 62(11):3005-3008, June 1, 2002.

Proteomics
  • Brown, K. and I. Jurisica. Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biology, 2007. In press.
  • Miriam Barrios-Rodiles, Kevin R. Brown, Barish Ozdamar, Rohit Bose, Zhong Liu, Robert S. Donovan, Fukiko Shinjo, Yongmei Liu, Joanna Dembowy, Ian W. Taylor, Valbona Luga, Natasa Przulj, Mark Robinson, Harukazu Suzuki, Yoshihide Hayashizaki, Igor Jurisica, and Jeffrey L. Wrana. High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells , Science 307:(5715): 1621-1625, 2005.
  • Brown, K. and I. Jurisica. Online Predicted Human Interaction Database OPHID, Bioinformatics, 2005. Advance Access published on January 18, 2005. doi:10.1093/bioinformatics/bti273. In press.
    • K. Brown and I. Jurisica. Online Predicted Human Interaction Database (OPHID): Exploring the human interactome. ISMB/ECCB'04, Glasgow, UK, 2004. Poster.
  • Przulj, N., Corneil, D., Jurisica, I. Modeling interactome: Scale-free or geometric?, Bioinformatics, 20(18):3508-3515, 2004
  • King, A. D., N. Przulj, Jurisica, I. Protein complex prediction via cost-based clustering. Bioinformatics, 20(17):3013-3020, 2004.
  • Przulj, N., Wigle, D., Jurisica, I. Functional topology in a network of protein interactions. Bioinformatics 20(3):340-348, 2004.

  • Software Tools
    • Network Analysis, Visualization adn Graphing at Toronto
    • Sultan, M., Wigle, D., Cumbaa, C., Maziarz, M., Glasgow, J., M.-S. Tsao, Jurisica, I. Binary tree-structured vector quantization approach to clustering and visualizing microarray data. Bioinformatics. Special Issue of ISMB'02, 18(Suppl. 1):S111-S119, 2002. Binary tree-structured vector quantization (BTSVQ) is a technique for analysis and visualization of highly multidimensional data. It combines partitive k-means clustering (for the analysis of low dimension, i.e., patient or biological sample space) with a tree-structured vector quantization (for the analysis of high dimension, i.e., gene space). In contrast to existing systems, our approach is less sensitive to data pre-processing and data normalization. In addition, the clustering results produced by the technique have strong similarities to that of self-organizing maps (SOMs). This hybrid technique has revealed clinically and biologically significant clusters in microarray data sets.

      The BTSVQ software is protected by copyright and trade-mark and may not be reproduced in any form or reverse engineered without prior written permission. However, you may download and use BTSVQ TM at your own risk only for non-commercial, personal or educational use. Authors and University Health Network must be acknowledged as the source of BTSVQ. Permission to use BTSVQ for commercial purposes must be obtained from University Health Network. By downloading and/or using BTSVQ you are agreeing to these terms.

      BTSVQ has been developed at Ontario Cancer Institute/University Health Network by Mujahid Sultan in Matlab R12, using the the SOM toolbox

      For code availability, please contact us.

    • Jurisica, I., J. Glasgow, and J. Mylopoulos. Incremental Iterative Retrieval and Browsing for Efficient Conversational CBR Systems. International Journal of Applied Intelligence. 12(3): 251-268, 2000.
    Updated January 2005