Higuchi T - Search Results

1

Clustering samples characterized by time course gene expression profiles using the mixture of state space models.

Hirose O, Yoshida R, Yamaguchi R, Imoto S, Higuchi T, Miyano S. Hirose O, et al. Among authors: higuchi t. Genome Inform. 2007;18:258-66. Genome Inform. 2007. PMID: 18546493

2

Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching.

Yoshida R, Imoto S, Higuchi T. Yoshida R, et al. Among authors: higuchi t. Proc IEEE Comput Syst Bioinform Conf. 2005:289-98. doi: 10.1109/csb.2005.32. Proc IEEE Comput Syst Bioinform Conf. 2005. PMID: 16447986

3

A mixed factors model for dimension reduction and extraction of a group structure in gene expression data.

Yoshida R, Higuchi T, Imoto S. Yoshida R, et al. Among authors: higuchi t. Proc IEEE Comput Syst Bioinform Conf. 2004:161-72. doi: 10.1109/csb.2004.1332429. Proc IEEE Comput Syst Bioinform Conf. 2004. PMID: 16448010

4

ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles.

Yoshida R, Higuchi T, Imoto S, Miyano S. Yoshida R, et al. Among authors: higuchi t. Bioinformatics. 2006 Jun 15;22(12):1538-9. doi: 10.1093/bioinformatics/btl129. Epub 2006 Apr 10. Bioinformatics. 2006. PMID: 16606685

5

Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data.

Nagasaki M, Yamaguchi R, Yoshida R, Imoto S, Doi A, Tamada Y, Matsuno H, Miyano S, Higuchi T. Nagasaki M, et al. Among authors: higuchi t. Genome Inform. 2006;17(1):46-61. Genome Inform. 2006. PMID: 17503355

6

Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models.

Hirose O, Yoshida R, Imoto S, Yamaguchi R, Higuchi T, Charnock-Jones DS, Print C, Miyano S. Hirose O, et al. Among authors: higuchi t. Bioinformatics. 2008 Apr 1;24(7):932-42. doi: 10.1093/bioinformatics/btm639. Epub 2008 Feb 21. Bioinformatics. 2008. PMID: 18292116

7

State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast.

Yamaguchi R, Higuchi T. Yamaguchi R, et al. Among authors: higuchi t. Int J Data Min Bioinform. 2006;1(1):77-87. doi: 10.1504/ijdmb.2006.009922. Int J Data Min Bioinform. 2006. PMID: 18402043

8

Predicting differences in gene regulatory systems by state space models.

Yamaguchi R, Imoto S, Yamauchi M, Nagasaki M, Yoshida R, Shimamura T, Hatanaka Y, Ueno K, Higuchi T, Gotoh N, Miyano S. Yamaguchi R, et al. Among authors: higuchi t. Genome Inform. 2008;21:101-13. Genome Inform. 2008. PMID: 19425151 Free article.

9

A state space representation of VAR models with sparse learning for dynamic gene networks.

Kojima K, Yamaguchi R, Imoto S, Yamauchi M, Nagasaki M, Yoshida R, Shimamura T, Ueno K, Higuchi T, Gotoh N, Miyano S. Kojima K, et al. Among authors: higuchi t. Genome Inform. 2010 Jan;22:56-68. Genome Inform. 2010. PMID: 20238419

10

Combining microarrays and biological knowledge for estimating gene networks via bayesian networks.

Imoto S, Higuchi T, Goto T, Tashiro K, Kuhara S, Miyano S. Imoto S, et al. Among authors: higuchi t. J Bioinform Comput Biol. 2004 Mar;2(1):77-98. doi: 10.1142/s021972000400048x. J Bioinform Comput Biol. 2004. PMID: 15272434

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