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Satoru Miyano

Researcher at Tokyo Medical and Dental University

Publications -  874
Citations -  45801

Satoru Miyano is an academic researcher from Tokyo Medical and Dental University. The author has contributed to research in topics: Gene & Gene regulatory network. The author has an hindex of 84, co-authored 811 publications receiving 38723 citations. Previous affiliations of Satoru Miyano include University of Paderborn & Institute of Medical Science.

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A novel ASXL1-OGT axis plays roles in H3K4 methylation and tumor suppression in myeloid malignancies.

TL;DR: It is demonstrated that ASXL1 is a part of a protein complex containing HCFC1 and OGT; OGT directly stabilizes AS XL1 by O-GlcNAcylation, and data suggest that MLL5, a knownHCFC1/OGT-interacting protein, is responsible for gene activation by the ASXL 1–OGT axis.
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A microarray analysis of gnotobiotic mice indicating that microbial exposure during the neonatal period plays an essential role in immune system development

TL;DR: Hundreds of genes were affected in all tissues in each of the colonized models; however, a gene set enrichment analysis method, MetaGene Profiler, demonstrated that the specific changes of Gene Ontology (GO) categories occurred predominantly in 0WexGF LI, SPF SI, and 5Wex GF SPL, respectively.
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Characterization of HBV integration patterns and timing in liver cancer and HBV-infected livers.

TL;DR: Overall HBV integration sites in clinical samples were significantly enriched in regions annotated as exhibiting open chromatin, a high level of gene expression, and early replication timing in liver cells, indicating thatHBV integration in liver tissue was biased according to chromatin accessibility, with additional selection pressures in the gene promoters of tumor samples.
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Petri Net Based Descriptions for Systematic Understanding of Biological Pathways

TL;DR: An overview of Petri net formalisms to describe biological pathways is given and the examples that analyze fundamental properties of biological pathways using T-invariant, P-invariaant, siphon, and trap are illustrated.
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Estimating gene regulatory networks and protein–protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

TL;DR: Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, the role of several genes whose functions are currently unknown are predicted and the probabilistic model can detect false positives of high-throughput data, such as yeast two-hybrid data.