Seiya Imoto
Seiya Imoto
Professor of Institute of Medical Science, University of Tokyo
Verified email at
Cited by
Cited by
Open source clustering software
MJL De Hoon, S Imoto, J Nolan, S Miyano
Bioinformatics 20 (9), 1453-1454, 2004
Dynamic linear models with Markov-switching
CJ Kim
Journal of Econometrics 60 (1-2), 1-22, 1994
Long noncoding RNA HOTAIR regulates polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers
R Kogo, T Shimamura, K Mimori, K Kawahara, S Imoto, T Sudo, F Tanaka, ...
Cancer research 71 (20), 6320-6326, 2011
Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression
S Imoto, T Goto, S Miyano
Biocomputing 2002, 175-186, 2001
Inferring gene networks from time series microarray data using dynamic Bayesian networks
SY Kim, S Imoto, S Miyano
Briefings in bioinformatics 4 (3), 228-235, 2003
Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks
S Imoto, T Higuchi, T Goto, K Tashiro, S Kuhara, S Miyano
Journal of bioinformatics and computational biology 2 (01), 77-98, 2004
Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data
S Kim, S Imoto, S Miyano
Biosystems 75 (1-3), 57-65, 2004
Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations
MJL De Hoon, S Imoto, K Kobayashi, N Ogasawara, S Miyano
Biocomputing 2003, 17-28, 2002
Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection
Y Tamada, SY Kim, H Bannai, S Imoto, K Tashiro, S Kuhara, S Miyano
Bioinformatics 19 (suppl_2), ii227-ii236, 2003
Plastin3 is a novel marker for circulating tumor cells undergoing the epithelial–mesenchymal transition and is associated with colorectal cancer prognosis
T Yokobori, H Iinuma, T Shimamura, S Imoto, K Sugimachi, H Ishii, ...
Cancer research 73 (7), 2059-2069, 2013
Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network
S Imoto, S Kim, T Goto, S Aburatani, K Tashiro, S Kuhara, S Miyano
Journal of bioinformatics and computational biology 1 (02), 231-252, 2003
A top-r feature selection algorithm for microarray gene expression data
A Sharma, S Imoto, S Miyano
IEEE/ACM Transactions on Computational Biology and Bioinformatics 9 (3), 754-764, 2011
Finding optimal models for small gene networks
S Ott, S Imoto, S Miyano
Biocomputing 2004, 557-567, 2003
Genomic landscape of esophageal squamous cell carcinoma in a Japanese population
G Sawada, A Niida, R Uchi, H Hirata, T Shimamura, Y Suzuki, Y Shiraishi, ...
Gastroenterology 150 (5), 1171-1182, 2016
Bayesian information criteria and smoothing parameter selection in radial basis function networks
S Konishi, T Ando, S Imoto
Biometrika 91 (1), 27-43, 2004
Finding optimal Bayesian network given a super-structure
E Perrier, S Imoto, S Miyano
Journal of Machine Learning Research 9 (Oct), 2251-2286, 2008
Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks
N Nariai, S Kim, S Imoto, S Miyano
Biocomputing 2004, 336-347, 2003
Major improvements to the Heliconius melpomene genome assembly used to confirm 10 chromosome fusion events in 6 million years of butterfly evolution
JW Davey, M Chouteau, SL Barker, L Maroja, SW Baxter, F Simpson, ...
G3: Genes, Genomes, Genetics 6 (3), 695-708, 2016
Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models
O Hirose, R Yoshida, S Imoto, R Yamaguchi, T Higuchi, ...
Bioinformatics 24 (7), 932-942, 2008
Statistical analysis of a small set of time-ordered gene expression data using linear splines
MJL de Hoon, S Imoto, S Miyano
Bioinformatics 18 (11), 1477-1485, 2002
The system can't perform the operation now. Try again later.
Articles 1–20