The following staffs supervise students:

• Satoru Miyano, Professor, Head of laboratory, Bioinformatics, Systems Biology
• Seiya Imoto, Associate Professor, Statistics, Bioinformatics, Statistical Analysis of Genomic Data
• Masao Nagasaki, Assistant Professor, Bioinformatics, Systems Biology, Algorithm, Software Development
• Rui Yamaguchi, Project Lecturer, Statistics, Bioinformatics, Computational Science, Systems Biology
• Yoshinori Tamada, Project Assistant Professor, Bioinformatics, Computational Science, Systems Biology

The student need to have a seminar by using textbook or papers of interest. Discussion in the seminar is strict, but it is really necessary to understand and progress research. The products from the seminar should publish as research papers that the student writes as the first author to international conferences or journals. For students who want to be a PhD course, we supervise him(her) to be a researcher who can define and solve problems by him(her)self. In past five years, we use the following textbooks in the seminar:

• Multivariate Analysis, K.V. Mardia, J.T. Kent and J.M. Bibby, Academic Press, 1980.
• Combinatorial Optimization, A. Schrijver, Springer, 2003.
• Algorithmics for Hard Problems, J. Hromkovic, Springer, 2003.
• Learning Bayesian Networks, R.E. Neapolitan, Prentice Hall, 2003.
• Graph Drawing -Algorithms for the Visualization of Graphs-
• Selected Open Proglems in Graph Drawing, GraphDrawing, 2003.
• Statistical Inference from Time-Cource Experimental Data
• Bayesian Methods for Nonlinear Classification and Regression, D.G.T. Denison et al., Wiley, 2002.

In recent years, the following peer reviewed papers were written by students as the first authors:

• O. Hirose, R. Yoshida, S. Imoto, R. Yamaguchi, T. Higuchi, Stephen D. Charnock-Jones, C. Print, S. Miyano (2008) Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models, Bioinformatics, accepted.
• O. Hirose, R. Yoshida, R. Yamaguchi, S. Imoto, T. Higuchi and S. Miyano (2008) Analyzing time course gene expression data with biological and technical replicates to estimate gene networks by state space models, Proc. 2nd Asia International Conference on Modelling & Simulation, in press. (AMS2008: Refereed conference)
• K. Kojima, M. Nagasaki, S. Miyano (2008) Fast grid la yout algorithm for biological networks with sweep calculation. Bioinformatices, accepted.

• O. Hirose, R. Yoshida, R. Yamaguchi, S. Imoto, T. Higuchi, S. Miyano (2007) Clustering with time course gene expression profiles and the mixture of state space models. Genome Informatics, 18, 258-266.
• K. Numata, S. Imoto, and S. Miyano (2007) A structure learning algorithm for inference of gene networks from microarray gene expression data using Bayesian networks. Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering, 1280-1284.
• K. Kojima, M. Nagasaki, E. Jeong, M. Kato and S. Miyano, An efficient grid layout algorithm for biological networks utilizing various biological attributes, BMC Bioinfomatics, 8(1), 1-76.

• ————

• O. Hirose, N. Nariai, Y. Tamada, H. Bannai, S. Imoto and S. Miyano (2005) Estimating gene networks from expression data and binding location data via boolean networks. Proc. 1st International Workshop on Data Mining and Bioinformatics, Lecture Note in Comupter Science, 3482, 349-356, Springer-Verlag.
• Y. Tamada, H. Bannai, S. Imoto, T. Katayama, M. Kanehisa and S. Miyano (2005) Utilizing evolutionary information and gene expression data for estimating gene regulations with Bayesian network models. Journal of Bioinformatics and Computational Biology, 3(6), 1295-1313.
• Y. Tamada, S. Imoto, K. Tashiro, S. Kuhara and S. Miyano Identifying drug active pathways from gene networks estimated by gene expression data. Genome Informatics, 16(1), 182-191.
• N. Nariai, Y. Tamada, S. Imoto and S. Miyano (2005) Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics, 21 Suppl.2, ii206-ii212.
• M. Kato, M. Nagasaki, A. Doi and S. Miyano (2005) Automatic drawing of networks using cross cost and subcomponent data, enome Informatics 16(2), 22-31.

• N. Nariai, S. Kim, S. Imoto and S. Miyano (2004) Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks. Pacific Symposium on Biocomputing, 9, 336-347.
• S. Ott, S. Imoto and S. Miyano (2004) Finding optimal models for small gene networks. Pacific Symposium on Biocomputing, 9, 557-567.
• S. Kim, S. Imoto and S. Miyano. (2004) Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems, 75(1-3), 57-65.

• S. Kim, S. Imoto and S. Miyano (2003) Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Proc. 1st Computational Methods in Systems Biology, Lecture Note in Computer Science, 2602, 104-113, Springer-Verlag.
• Y. Tamada, S. Kim, H. Bannai, S. Imoto, K. Tashiro, S. Kuhara and S. Miyano (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics, 19 Suppl.2, ii227-ii236.
• S. Kim, S. Imoto and S. Miyano (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics, 4(3), 228-235.

Please refer to here.