Seiya Imoto
Seiya Imoto
Professor of Institute of Medical Science, University of Tokyo
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Citado por
Open source clustering software
MJL De Hoon, S Imoto, J Nolan, S Miyano
Bioinformatics 20 (9), 1453-1454, 2004
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
Mapping the human genetic architecture of COVID-19
Writing group Writing group leaders Pathak Gita A. 6 Andrews Shea J. 7 Kanai ...
Nature 600 (7889), 472-477, 2021
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 PrognosisPLS3 Is a New EMT …
T Yokobori, H Iinuma, T Shimamura, S Imoto, K Sugimachi, H Ishii, ...
Cancer research 73 (7), 2059-2069, 2013
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
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
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
Finding optimal models for small gene networks
S Ott, S Imoto, S Miyano
Biocomputing 2004, 557-567, 2003
Bayesian information criteria and smoothing parameter selection in radial basis function networks
S Konishi, T Ando, S Imoto
Biometrika 91 (1), 27-43, 2004
Blautia genus associated with visceral fat accumulation in adults 20–76 years of age
N Ozato, S Saito, T Yamaguchi, M Katashima, I Tokuda, K Sawada, ...
NPJ biofilms and microbiomes 5 (1), 1-9, 2019
Finding Optimal Bayesian Network Given a Super-Structure.
E Perrier, S Imoto, S Miyano
Journal of Machine Learning Research 9 (10), 2008
Pan-cancer analysis of whole genomes.
S Hirano, L Yang, M Juul, CA Purdie, BP O'Neill, R Kabbe, ...
Nature 578 (DKFZ-2020-01051), 82-93, 2020
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
Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma
M Yamauchi, R Yamaguchi, A Nakata, T Kohno, M Nagasaki, ...
Public Library of Science 7 (9), e43923, 2012
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Artículos 1–20