Ryo Yoshida

Summary

Affiliation: Institute of Statistical Mathematics
Country: Japan

Publications

  1. ncbi Bayesian learning of biological pathways on genomic data assimilation
    Ryo Yoshida
    Institute of Statistical Mathematics, Research Organization of Information and Systems, 4 6 7 Minami Azabu, Minato ku, Tokyo 106 8569, Japan
    Bioinformatics 24:2592-601. 2008
  2. ncbi Bayesian experts in exploring reaction kinetics of transcription circuits
    Ryo Yoshida
    The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tokyo, Japan
    Bioinformatics 26:i589-95. 2010
  3. ncbi A state space representation of VAR models with sparse learning for dynamic gene networks
    Kaname Kojima
    Human Genome Center, Institute of Medical Science, University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo 108 8639, Japan
    Genome Inform 22:56-68. 2010
  4. ncbi Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data
    Masao Nagasaki
    Human Genome Center, Institute of Medical Science, University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo, 108 8639, Japan
    Genome Inform 17:46-61. 2006
  5. ncbi ExonMiner: Web service for analysis of GeneChip Exon array data
    Kazuyuki Numata
    Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
    BMC Bioinformatics 9:494. 2008
  6. ncbi SiGN-SSM: open source parallel software for estimating gene networks with state space models
    Yoshinori Tamada
    Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo 108 8639, Japan
    Bioinformatics 27:1172-3. 2011
  7. ncbi Clustering samples characterized by time course gene expression profiles using the mixture of state space models
    Osamu Hirose
    Human Genome Center, Institute of Medical Science, University of Tokyo, Minato ku, Tokyo, 108 8639, Japan
    Genome Inform 18:258-66. 2007
  8. ncbi Predicting differences in gene regulatory systems by state space models
    Rui Yamaguchi
    Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Genome Inform 21:101-13. 2008
  9. ncbi Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models
    Osamu Hirose
    Human Genome Center, Institute of Medical Science, University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo, 108 8639, Japan
    Bioinformatics 24:932-42. 2008
  10. ncbi Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data
    Shinya Tasaki
    Medical Proteomics Laboratory, Institute of Medical Science, The University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo 108 8639, Japan
    Genome Inform 17:226-38. 2006

Collaborators

Detail Information

Publications16

  1. ncbi Bayesian learning of biological pathways on genomic data assimilation
    Ryo Yoshida
    Institute of Statistical Mathematics, Research Organization of Information and Systems, 4 6 7 Minami Azabu, Minato ku, Tokyo 106 8569, Japan
    Bioinformatics 24:2592-601. 2008
    ..Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision...
  2. ncbi Bayesian experts in exploring reaction kinetics of transcription circuits
    Ryo Yoshida
    The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tokyo, Japan
    Bioinformatics 26:i589-95. 2010
    ..Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics...
  3. ncbi A state space representation of VAR models with sparse learning for dynamic gene networks
    Kaname Kojima
    Human Genome Center, Institute of Medical Science, University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo 108 8639, Japan
    Genome Inform 22:56-68. 2010
    ....
  4. ncbi Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data
    Masao Nagasaki
    Human Genome Center, Institute of Medical Science, University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo, 108 8639, Japan
    Genome Inform 17:46-61. 2006
    ..We demonstrate the effectiveness of our approach using synthetic data. As a result, parameter estimation using genomic data assimilation works very well and the network structure is suitably selected...
  5. ncbi ExonMiner: Web service for analysis of GeneChip Exon array data
    Kazuyuki Numata
    Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
    BMC Bioinformatics 9:494. 2008
    ..However, exon array produces massive datasets that are more than we can handle and analyze on personal computer...
  6. ncbi SiGN-SSM: open source parallel software for estimating gene networks with state space models
    Yoshinori Tamada
    Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo 108 8639, Japan
    Bioinformatics 27:1172-3. 2011
    ..The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. CONTACT: tamada@ims.u-tokyo.ac.jp...
  7. ncbi Clustering samples characterized by time course gene expression profiles using the mixture of state space models
    Osamu Hirose
    Human Genome Center, Institute of Medical Science, University of Tokyo, Minato ku, Tokyo, 108 8639, Japan
    Genome Inform 18:258-66. 2007
    ..We demonstrate the proposed method along with the cluster analysis of 53 multiple sclerosis patients under recombinant interferon beta therapy with the longitudinal time course expression profiles...
  8. ncbi Predicting differences in gene regulatory systems by state space models
    Rui Yamaguchi
    Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Genome Inform 21:101-13. 2008
    ..We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes...
  9. ncbi Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models
    Osamu Hirose
    Human Genome Center, Institute of Medical Science, University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo, 108 8639, Japan
    Bioinformatics 24:932-42. 2008
    ..One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway...
  10. ncbi Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data
    Shinya Tasaki
    Medical Proteomics Laboratory, Institute of Medical Science, The University of Tokyo, 4 6 1 Shirokanedai, Minato ku, Tokyo 108 8639, Japan
    Genome Inform 17:226-38. 2006
    ..By using the proteomic data, we semi-automatically constructed a well-tuned EGFR HFPNe model by using the Cell Illustrator coupled with the DA framework...
  11. ncbi A statistical framework for genome-wide discovery of biomarker splice variations with GeneChip Human Exon 1.0 ST Arrays
    Ryo Yoshida
    Human Genome Center, Institute of Medical Science, University of Tokyo, 4 6 1 Shirokanedai, Minato ku, 108 8639 Tokyo, Japan
    Genome Inform 17:88-99. 2006
    ..Our work is an important first step toward development of more advanced statistical technology. Supplementary information and materials are available from http://bonsai.ims.u-tokyo.ac.jp/~yoshidar/IBSB2006_ExonArray.htm...
  12. ncbi Identification of activated transcription factors from microarray gene expression data of Kampo medicine-treated mice
    Rui Yamaguchi
    Human Genome Center, Institute of Medical Science, University of Tokyo, Minato ku, Tokyo 108 8639, Japan
    Genome Inform 18:119-29. 2007
    ..Our method gives summary for the system's behavior with various functional annotations, e.g. TFAs and gene ontology, and thus offer clues to understand it in more holistic manner...
  13. ncbi ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles
    Ryo Yoshida
    Human Genome Center, Institute of Medical Science, University of Tokyo 4 6 1 Shirokanedai, Minato ku, Tokyo 108 8639, Japan
    Bioinformatics 22:1538-9. 2006
    ..It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on...
  14. ncbi Direction control of chemical wave propagation in self-oscillating gel array
    Shinji Tateyama
    Department of Materials Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
    J Phys Chem B 112:1777-82. 2008
    ..The swelling and deswelling changes of the gels followed the unidirectional propagation of the chemical wave...
  15. ncbi Effect of initial substrate concentration of the Belousov-Zhabotinsky reaction on self-oscillation for microgel system
    Daisuke Suzuki
    Department of Materials Engineering, Graduate School of Engineering, The University of Tokyo, 7 3 1 Hongo, Bunkyo ku, Tokyo, Japan
    J Phys Chem B 112:12618-24. 2008
    ..The change in oscillation for the microgels can be understood by considering the microgel network effect...
  16. ncbi Characterization of autonomously oscillating viscosity induced by swelling/deswelling oscillation of the microgels
    Hajime Taniguchi
    Department of Materials Engineering, Graduate School of Engineering, The University of Tokyo, 7 3 1 Hongo, Bunkyo ku, Tokyo 113 8656, Japan, International Young Researchers Empowerment Center, Shinshu University, 3 15 1 Tokida, Ueda, Nagano 386 8567, Japan, and PRESTO, Japan Science and Technology Agency, 4 1 8 Honcho Kawaguchi, Saitama, Japan
    J Phys Chem B 114:2405-10. 2010
    ....