Sayaka Mizutani Ph.D student Department of Biophysics, Devision of Biological Sciences Graduate School of Science, Kyoto University Bioinformatics Center, Institute for Chemical Research Kyoto University, Gokasho, Uji, 611-0011 Kyoto, Japan URL: http://cls.kuicr.kyoto-u.ac.jp Tel: +81-774-38-3270

Education

Publications

1. Hiroaki Iwata, Ryusuke Sawada, Sayaka Mizutani, and Yoshihiro Yamanishi.
   Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data.
   J. Chem. Inf. Model., January 20 (2015).
   [paper].
   
2. Sayaka Mizutani, Yousuke Noro, Masaaki Kotera, and Susumu Goto.
   Pharmacoepidemiological characterization of drug-inducedadverse reaction clusters towards understanding of their mechanisms.
   Computational biology and chemistry, Jan 24 (2014).
   [paper].
   
3. Hiroaki Iwata, Sayaka Mizutani, Yasuo Tabei, Masaaki Kotera, Susumu Goto, and Yoshihiro Yamanishi.
   Inferring protein domains associated with drug side effects based on drug-target interaction network.
   BMC Syst Biol 7(S6), S18 (2013).
   [paper]
4. Sayaka Mizutani, Edouard Pauwels, Veronique Stoven, Susumu Goto, and Yoshihiro Yamanishi.
   Relating drug-protein interaction network with drug side effects.
   Bioinformatics 28, i522-i528 (2012).
   [paper]
5. Sayaka Mizutani, Michihiro Tanaka, Craig Wheelock, Minoru Kanehisa, and Susumu Goto.
   Phylogenetic analysis of lipid mediator GPCRs.
   Genome Informatics 24, 116-126 (2010).
   [PubMed]
6. Shawn Cokus, Sayaka Mizutani, and Matteo Pellegrini.
   An improved method for identifying functionally linked proteins using phylogenetic profiles.
   BMC Bioinformatics 8(S4), S7 (2007).
   [paper]

Awards

1. Graduate Student Presentation Day, February 27, Institute for Chemical Research, Kyoto University, Kyoto, Japan
   Oral Presentation Award
   Sayaka Mizutani
   
2. Japanese Society for Bioinformatics (JSBi) 2013, October 29-31, Kyoto, Japan
   Excellent Poster Award
   Sayaka Mizutani, Yousuke Noro, Masaaki Kotera, and Susumu Goto.
   A biclustering approach to associating adverse drug events with the patients' physiological backgrounds.