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- | ====== OVERVIEW ====== | + | ===== Staff and Mentorship ===== |

- | The aim of the research at this laboratory is to investigate and develop knowledge information processing systems for knowledge discovery,information interpretation and knowledge bases that deal with biological information about gene expression data,nucleic acid sequences and proteins.The following three topics are mutually allied to pathway and gene networks analyses on computer. | ||

- | ===== Gene expression profile analysis ===== | ||

- | For inferring the genetic network from gene expression profile data,various algorithms for analyzing the network are being developed.In particular,we have realized a novel gene network inference method based on Bayesian network and nonparametric regression,a visulalized gene network analysis system together with the knowledge base of the genetic network of organisms,and a clustering software library (Fig.1). | ||

- | ===== Knowledge discovery system ===== | ||

- | We have been developing a system Hypotheis Creator(HC) for assisting knowledge discovery from complete genomes,SNP data,gene expression profile data,protein data. With this concept,simultaneously,we have been conducting various computational knowledge dicoveries for protein localization prediction extraction and aberrant splicing. | ||

+ | ==== Staff ==== | ||

+ | 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 | ||

- | ===== Modeling and simulation of biopathways ===== | ||

- | As one of the topics in Systems Biolgy,we have been creating an computational environment for modeling and simulation of biopathways in cells and organisms focused on gene regulatory networks, signaling pathways,metabolic pathways, and physical simulations,etc.With this approach,the functions of genes and systems of genes will be analyzed and predicted.This research is realized as a software Genomic Object Net (http://www.cellillustrator.com) (Fig.2). | ||

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- | ==== (Fig.1) ==== | ||

- | {{ci_img_1.png|}} | ||

- | An example of gene network analysis. This figure shows a network of yeast genes estimated by microarray data. | ||

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- | ==== (Fig.2) ==== | ||

- | {{ci_img_2.png|}} | ||

- | Genomic Object Net realizes smooth modeling of gene regulatory networks, metabolic pathways, signaling pathways, etc.XML technology is employed to create a personally visualized simulation environment.The picture shows simulation of Fas ligand induced apoptosis signaling pathway. | + | ==== Mentorship ==== |

+ | | ||

+ | 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: | ||

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+ | * //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. | ||

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

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+ | ==== 2008 ==== | ||

+ | * 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. | ||

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+ | ==== 2007 ==== | ||

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+ | * 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. | ||

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+ | ==== 2006 ==== | ||

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+ | ==== 2005 ==== | ||

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+ | * 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. | ||

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+ | ==== 2004 ==== | ||

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+ | * 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. | ||

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+ | ==== 2003 ==== | ||

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+ | * 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. | ||

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+ | ===== After University ===== | ||

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+ | Please refer to [[sinro|here]]. | ||

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