Faculty Information, Institute for Chemical Research, Kyoto University   [ English | Japanese ]


TAKIGAWA, Ichigaku

Bioinformatics Center - Bio-knowledge Engineering -
Assistant Professor
Ph.D.
URL:http://www.bic.kyoto-u.ac.jp/pathway/takigawa/
E-mail: takigawa@kuicr.kyoto-u.ac.jp
Tel: 0774-38-3024
Fax:
Academic career
    Aug 2005-: Assistant Professor, Bioinformatics Center, Institute for Chemical Research, Kyoto University
    Apr 2005-Jul 2005: COE Research Associate, Bioinformatics Center, Institute for Chemical Research, Kyoto University
    Apr 2004-Mar 2005: COE Postdoctoral Fellow, Hokkaido University
    Mar 2004: Ph.D., Hokkaido University
Research field
    Probabilistic pattern pecognition, Statistical signal processing, Mathematical optimization, Compressed sensing, Statistical/computational learning theory, Probabilities on discrete/combinatorial structures, Random structures, Structural pattern enumeration. Complex molecular interactions, Computational genomics, Computational genetics, Chemical genomics, and Pharmacogenomics.
Current research
Selected publications
  1. I. Takigawa, M. Kudo and A. Nakamura, The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets Lecture Notes in Computer Science (Machine Learning and Data Mining in Pattern Recognition), 3587, 90-99 (2005)
  2. I. Takigawa, M.Kudo and J.Toyama, Performance Analysis of Minimum l1-Norm Solutions for Underdetermined Source Separation IEEE Transactions on Signal Processing 52(3), 582-591 (2004).
  3. I. Takigawa, M. Kudo, A. Nakamura and J. Toyama, On the Minimum L1-Norm Signal Recovery in Underdetermined Source Separation Lecture Notes in Computer Science (Independent Component Analysis and Blind Signal Separation), 3195, 193-200 (2004)
  4. I. Takigawa, N.Abe, Y.Shidara and M.Kudo, The Boosted/Bagged Subclass Method International Journal of Computing Anticipatory Systems 14, 311-320 (2004)
  5. A. Tanaka, I. Takigawa, H. Imai, M.Kudo and M.Miyakoshi, Projection Learning Based Kernel Machine Design Using Series of Monotone Increasing Reproducing Kernel Hilbert Spaces Lecture Notes in Computer Science (Several Aspects in Ubiquitous Pattern Recognition Techniques) 3213, 1058-1064 (2004)
  6. I. Takigawa, J. Toyama, M. Kudo and M.Shimbo, Error Analysis of MAP Solutions under Laplace Prior in Underdetermined Blind Source Separation Proceedings of International ICSC Symposium on Advances in Intelligent Data Analysis (AIDA'01), Bangor, U.K. (2001).
  7. I. Takigawa, J. Toyama, M. Kudo and M. Shimbo, A Modified LEGION Using a Spectrogram for Speech Segregation Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC'99) Tokyo, Japan (1999)
  8. I. Takigawa, M. Kudo and A. Nakamura, Subclass Covering by Balls for Pattern Classification Proceedings of The 2nd International Workshop on Ubiguitous Knowledge Network Environment, Sapporo, March 16-18 (2005)
  9. I. Takigawa, N. Abe, Y. Shidara and M. Kudo, The Subclass Method Using Adaptive Sampling Proceedings of The 1st International Workshop on Ubiguitous Knowledge Network Environment, Sapporo, November 25-27 (2003).
  10. Kayano, M.; Takigawa, I.; Shiga, M.; Tsuda, K.; Mamitsuka, H., ROS-DET: robust detector of switching mechanisms in gene expression Nucleic Acids Res. 39(11), e74 (2011)  [pubmed]
  11. Takigawa, I.; Tsuda, K.; Mamitsuka, H., Mining significant substructure pairs for interpreting polypharmacology in drug-target network PLoS One 6(2), e16999 (2011)  [pubmed]
  12. Takigawa, I.; Mamitsuka, H., Efficiently mining delta-tolerance closed frequent subgraphs Machine Learning 82(2), 95-121 (2011)
  13. Shiga, M.; Takigawa, I.; Mamitsuka, H., A spectral approach to clustering numerical vectors as nodes in network Pattern Recognition 44(2), 236-251 (2011)

Update: May 07,2012