Teppei Shimamura

发表

D3M: Detection of differential distributions of methylation levels

Satoru Miyano, Yusuke Matsui, Masahiro Mizuta, 2015, bioRxiv.

Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

A network of networks approach for modeling interconnected brain tissue-specific networks

Yusuke Matsui, Teppei Shimamura, Hideko Kawakubo, 2018, bioRxiv.

Hierarchical non-negative matrix factorization using clinical information for microbial communities

Teppei Shimamura, Ko Abe, Masaaki Hirayama, 2019, BMC Genomics.

A network of networks approach for modeling interconnected brain tissue-specific networks

Yusuke Matsui, Teppei Shimamura, Hideko Kawakubo, 2019, Bioinform..

Model-based cell clustering and population tracking for time-series flow cytometry data

Teppei Shimamura, Yuka Maeda, Ko Abe, 2019, BMC Bioinformatics.

Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks

Satoru Miyano, Tatsuya Akutsu, Teppei Shimamura, 2015, BMC Systems Biology.

Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks

Satoru Miyano, Teppei Shimamura, Heewon Park, 2018, J. Comput. Biol..

PREDICTING DIFFERENCES IN GENE REGULATORY SYSTEMS BY STATE SPACE MODELS

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2008 .

A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models

Satoru Miyano, Tatsuya Akutsu, Teppei Shimamura, 2016, Comput. Stat. Data Anal..

Recursive regularization for inferring gene networks from time-course gene expression profiles

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2009, BMC Systems Biology.

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

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2010, Genome informatics. International Conference on Genome Informatics.

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

Satoru Miyano, Yusuke Matsui, Satoshi Ito, 2017, CIBB.

Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical Methods

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Collocation-based sparse estimation for constructing dynamic gene networks.

Masao Nagasaki, Satoru Miyano, André Fujita, 2010, Genome informatics. International Conference on Genome Informatics.

Discovering modulators of gene regulation by local energy statistics

Yusuke Matsui, Teppei Shimamura, 2015 .

Sign: large-scale gene network estimation environment for high performance computing.

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2011, Genome informatics. International Conference on Genome Informatics.

Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data.

Satoru Miyano, Teppei Shimamura, Seiya Imoto, 2007, Genome informatics. International Conference on Genome Informatics.

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

Satoru Miyano, Yusuke Matsui, Teppei Shimamura, 2017, bioRxiv.

Discovery of Exogenous Variables in Data with More Variables Than Observations

Aapo Hyvärinen, Teppei Shimamura, Takashi Washio, 2009, ICANN.

Inferring Contagion in Regulatory Networks

João Ricardo Sato, Carlos Eduardo Ferreira, Satoru Miyano, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

A latent allocation model for the analysis of microbial composition and disease

Teppei Shimamura, Ko Abe, Kinji Ohno, 2018, bioRxiv.

CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data

Teppei Shimamura, Yuka Maeda, Ko Abe, 2020, Bioinform..

Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data.

Satoru Miyano, André Fujita, Teppei Shimamura, 2008, Genome informatics. International Conference on Genome Informatics.

Statistical model-based testing to evaluate the recurrence of genomic aberrations

Satoru Miyano, Teppei Shimamura, Atsushi Niida, 2012, Bioinform..

High performance computing of a fusion gene detection pipeline on the K computer

Satoru Miyano, Yuichi Shiraishi, Satoshi Ito, 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Estimating exogenous variables in data with more variables than observations

Aapo Hyvärinen, Teppei Shimamura, Takashi Washio, 2011, Neural Networks.

A Latent Allocation Model for the Analysis of Microbial Composition and Disease

Teppei Shimamura, Ko Abe, Masaaki Hirayama, 2018 .

Model-based cell clustering and population tracking for time-series flow cytometry data

Teppei Shimamura, Yuka Maeda, Ko Abe, 2019, BMC Bioinformatics.

Introduction of TOP500 supercomputers for bioinformaticians -Case study of GFK-

Satoru Miyano, Yuichi Shiraishi, Teppei Shimamura, 2015 .

phyC: Clustering cancer evolutionary trees

Satoru Miyano, Yusuke Matsui, Teppei Shimamura, 2016, bioRxiv.

Tumor Subclonal Progression Model for Cancer Hallmark Acquisition

Satoru Miyano, Yusuke Matsui, Teppei Shimamura, 2017, CIBB.

Inferring dynamic gene networks under varying conditions for transcriptomic network comparison

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2010, Bioinform..

WEIGHTED LASSO IN GRAPHICAL GAUSSIAN MODELING FOR LARGE GENE NETWORK ESTIMATION BASED ON MICROARRAY DATA

Satoru Miyano, Teppei Shimamura, Seiya Imoto, 2007 .

Model-free unsupervised gene set screening based on information enrichment in expression profiles

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2010, Bioinform..

scMM: Mixture-of-Experts Multimodal Deep Generative Model for Single-Cell Multiomics Data Analysis

Teppei Shimamura, Ko Abe, Kodai Minoura, 2021 .

Dynamically and epigenetically coordinated GATA/ETS/SOX transcription factor expression is indispensable for endothelial cell differentiation

Teppei Shimamura, Hiroyuki Aburatani, Tatsuhiko Kodama, 2017, Nucleic acids research.

Integrated genetic and epigenetic analysis defines novel molecular subgroups in rhabdomyosarcoma

Satoru Miyano, Yuichi Shiraishi, Teppei Shimamura, 2015, Nature Communications.

D3M: Detection of differential distributions of methylation levels

Satoru Miyano, Masahiro Mizuta, Teppei Shimamura, 2015, bioRxiv.

A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2011, PloS one.

Recurrent mutations in multiple components of the cohesin complex in myeloid neoplasms

Masao Nagasaki, Satoru Miyano, Yuichi Shiraishi, 2012, Nature Genetics.

Predicting differences in gene regulatory systems by state space models.

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2008, Genome informatics. International Conference on Genome Informatics.

Dynamic Change of Chromatin Conformation in Response to Hypoxia Enhances the Expression of GLUT3 (SLC2A3) by Cooperative Interaction of Hypoxia-Inducible Factor 1 and KDM3A

Teppei Shimamura, Takahide Kohro, Takanori Fujita, 2012, Molecular and Cellular Biology.

Detection of differential distributions of methylation patterns

Satoru Miyano, Masahiro Mizuta, Teppei Shimamura, 2015 .

Epidermal Growth Factor Receptor Tyrosine Kinase Defines Critical Prognostic Genes of Stage I Lung Adenocarcinoma

Masao Nagasaki, Satoru Miyano, Teppei Shimamura, 2012, PloS one.

Mutational landscape and clonal architecture in grade II and III gliomas

Satoru Miyano, Yuichi Shiraishi, Toshihiko Wakabayashi, 2015, Nature Genetics.

Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer Evolution

Satoru Miyano, Yuichi Shiraishi, Teppei Shimamura, 2016, PLoS genetics.

Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker

Satoru Miyano, Teppei Shimamura, Seiya Imoto, 2014, PloS one.