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Seiya Imoto: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Reiichiro Nakamichi, Seiya Imoto, Satoru Miyano
    Case-Control Study of Binary Disease Trait Considering Interactions between SNPs and Environmental Effects using Logistic Regression. [Citation Graph (0, 0)][DBLP]
    BIBE, 2004, pp:73-78 [Conf]
  2. Seiya Imoto, Tomoyuki Higuchi, SunYong Kim, Euna Jeong, Satoru Miyano
    Residual Bootstrapping and Median Filtering for Robust Estimation of Gene Networks from Microarray Data. [Citation Graph (0, 0)][DBLP]
    CMSB, 2004, pp:149-160 [Conf]
  3. SunYong Kim, Seiya Imoto, Satoru Miyano
    Dynamic Bayesian Network and Nonparametric Regression for Nonlinear Modeling of Gene Networks from Time Series Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    CMSB, 2003, pp:104-113 [Conf]
  4. Seiya Imoto, Tomoyuki Higuchi, Takao Goto, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
    Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    CSB, 2003, pp:104-113 [Conf]
  5. Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Aburatani, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
    Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network. [Citation Graph (0, 0)][DBLP]
    CSB, 2002, pp:219-227 [Conf]
  6. Ryo Yoshida, Tomoyuki Higuchi, Seiya Imoto
    A Mixed Factors Model for Dimension Reduction and Extraction of a Group Structure in Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    CSB, 2004, pp:161-172 [Conf]
  7. Ryo Yoshida, Seiya Imoto, Tomoyuki Higuchi
    Estimating Time-Dependent Gene Networks from Time Series Microarray Data by Dynamic Linear Models with Markov Switching. [Citation Graph (0, 0)][DBLP]
    CSB, 2005, pp:289-298 [Conf]
  8. Michiel J. L. de Hoon, Seiya Imoto, Satoru Miyano
    Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data Using Differential Equations. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:267-274 [Conf]
  9. Yoshinori Tamada, SunYong Kim, Hideo Bannai, Seiya Imoto, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
    Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. [Citation Graph (0, 0)][DBLP]
    ECCB, 2003, pp:227-236 [Conf]
  10. Naoki Nariai, Yoshinori Tamada, Seiya Imoto, Satoru Miyano
    Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. [Citation Graph (0, 0)][DBLP]
    ECCB/JBI, 2005, pp:212- [Conf]
  11. Osamu Hirose, Naoki Nariai, Yoshinori Tamada, Hideo Bannai, Seiya Imoto, Satoru Miyano
    Estimating Gene Networks from Expression Data and Binding Location Data via Boolean Networks. [Citation Graph (0, 0)][DBLP]
    ICCSA (3), 2005, pp:349-356 [Conf]
  12. Ryo Yoshida, Seiya Imoto, Tomoyuki Higuchi
    A Penalized Likelihood Estimation on Transcriptional Module-Based Clustering. [Citation Graph (0, 0)][DBLP]
    ICCSA (3), 2005, pp:389-401 [Conf]
  13. Michiel J. L. de Hoon, Yuko Makita, Seiya Imoto, Kazuo Kobayashi, Naotake Ogasawara, Kenta Nakai, Satoru Miyano
    Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data. [Citation Graph (0, 0)][DBLP]
    ISMB/ECCB (Supplement of Bioinformatics), 2004, pp:101-108 [Conf]
  14. Tomohiro Ando, Seiya Imoto, Satoru Miyano
    Functional Data Analysis of the Dynamics of Gene Regulatory Networks. [Citation Graph (0, 0)][DBLP]
    KELSI, 2004, pp:69-83 [Conf]
  15. Michiel J. L. de Hoon, Seiya Imoto, Kazuo Kobayashi, Naotake Ogasawara, Satoru Miyano
    Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data of Bacillus Subtilis Using Differential Equations. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2003, pp:17-28 [Conf]
  16. Michiel J. L. de Hoon, Seiya Imoto, Kazuo Kobayashi, Naotake Ogasawara, Satoru Miyano
    Predicting the Operon Structure of Bacillus subtilis Using Operon Length, Intergene Distance, and Gene Expression Information. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2004, pp:276-287 [Conf]
  17. Seiya Imoto, Takao Goto, Satoru Miyano
    Estimation of Genetic Networks and Functional Structures Between Genes by Using Bayesian Networks and Nonparametric Regression. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2002, pp:175-186 [Conf]
  18. Seiya Imoto, Yoshinori Tamada, Hiromitsu Araki, Kaori Yasuda, Cristin G. Print, Stephen D. Charnock-Jones, Deborah Sanders, Christopher J. Savoie, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
    Computational Strategy for Discovering Druggable Gene Networks from Genome-Wide RNA Expression Profiles. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2006, pp:559-571 [Conf]
  19. Naoki Nariai, SunYong Kim, Seiya Imoto, Satoru Miyano
    Using Protein-Protein Interactions for Refining Gene Networks Estimated from Microarray Data by Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2004, pp:336-347 [Conf]
  20. Sascha Ott, Seiya Imoto, Satoru Miyano
    Finding Optimal Models for Small Gene Networks. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2004, pp:557-567 [Conf]
  21. SunYong Kim, Seiya Imoto, Satoru Miyano
    Inferring gene networks from time series microarray data using dynamic Bayesian networks. [Citation Graph (0, 0)][DBLP]
    Briefings in Bioinformatics, 2003, v:4, n:3, pp:228- [Journal]
  22. Michiel J. L. de Hoon, Seiya Imoto, Satoru Miyano
    Statistical analysis of a small set of time-ordered gene expression data using linear splines. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2002, v:18, n:11, pp:1477-1485 [Journal]
  23. Ryo Yoshida, Tomoyuki Higuchi, Seiya Imoto, Satoru Miyano
    ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:12, pp:1538-1539 [Journal]
  24. Reiichiro Nakamichi, Seiya Imoto, Satoru Miyano
    Statistical Model Selection Method to Analyze Combinatorial Effects of Snps and Environmental Factors for Binary Disease. [Citation Graph (0, 0)][DBLP]
    International Journal on Artificial Intelligence Tools, 2006, v:15, n:5, pp:711-724 [Journal]
  25. Seiya Imoto, Tomoyuki Higuchi, Takao Goto, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
    Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    J. Bioinformatics and Computational Biology, 2004, v:2, n:1, pp:77-98 [Journal]
  26. Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Aburatani, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
    Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network. [Citation Graph (0, 0)][DBLP]
    J. Bioinformatics and Computational Biology, 2003, v:1, n:2, pp:231-252 [Journal]
  27. Seiya Imoto, Christopher J. Savoie, Sachiyo Aburatani, SunYong Kim, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
    Use of Gene Networks for Identifying and Validating Drug Targets. [Citation Graph (0, 0)][DBLP]
    J. Bioinformatics and Computational Biology, 2003, v:1, n:3, pp:459-474 [Journal]
  28. Yoshinori Tamada, Hideo Bannai, Seiya Imoto, Toshiaki Katayama, Minoru Kanehisa, Satoru Miyano
    Utilizing Evolutionary Information and Gene Expression Data for Estimating Gene Networks with Bayesian Network Models. [Citation Graph (0, 0)][DBLP]
    J. Bioinformatics and Computational Biology, 2005, v:3, n:6, pp:1295-1314 [Journal]
  29. Pramod K. Gupta, Ryo Yoshida, Seiya Imoto, Rui Yamaguchi, Satoru Miyano
    Statistical Absolute Evaluation of Gene Ontology Terms with Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    ISBRA, 2007, pp:146-157 [Conf]

  30. Computational Genome-Wide Discovery of Aberrant Splice Variations with Exon Expression Profiles. [Citation Graph (, )][DBLP]


  31. A Structure Learning Algorithm for Inference of Gene Networks from Microarray Gene Expression Data Using Bayesian Networks. [Citation Graph (, )][DBLP]


  32. Identifying Hidden Confounders in Gene Networks by Bayesian Networks. [Citation Graph (, )][DBLP]


  33. Discovery of Exogenous Variables in Data with More Variables Than Observations. [Citation Graph (, )][DBLP]


  34. Analyzing Time Course Gene Expression Data with Biological and Technical Replicates to Estimate Gene Networks by State Space Models. [Citation Graph (, )][DBLP]


  35. DIGDAG, a First Algorithm to Mine Closed Frequent Embedded Sub-DAGs. [Citation Graph (, )][DBLP]


  36. Partial Order-Based Bayesian Network Learning Algorithm for Estimating Gene Networks. [Citation Graph (, )][DBLP]


  37. Gene Networks Viewed through Two Models. [Citation Graph (, )][DBLP]


  38. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. [Citation Graph (, )][DBLP]


  39. Bayesian learning of biological pathways on genomic data assimilation. [Citation Graph (, )][DBLP]


  40. Inferring dynamic gene networks under varying conditions for transcriptomic network comparison. [Citation Graph (, )][DBLP]


  41. Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells. [Citation Graph (, )][DBLP]


  42. ExonMiner: Web service for analysis of GeneChip Exon array data. [Citation Graph (, )][DBLP]


  43. Gene set-based module discovery in the breast cancer transcriptome. [Citation Graph (, )][DBLP]


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