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Hiroshi Tanaka

Researcher at Tokyo Medical and Dental University

Publications -  223
Citations -  5930

Hiroshi Tanaka is an academic researcher from Tokyo Medical and Dental University. The author has contributed to research in topics: Rough set & Cancer. The author has an hindex of 33, co-authored 222 publications receiving 5312 citations. Previous affiliations of Hiroshi Tanaka include Hyogo College of Medicine & Tohoku University.

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A promoter-level mammalian expression atlas

Alistair R. R. Forrest, +280 more
- 27 Mar 2014 - 
TL;DR: For example, the authors mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body.
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Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones

Tadashi Imanishi, +167 more
- 20 Apr 2004 - 
TL;DR: The H-InvDB as discussed by the authors is a database of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level.
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Nuclear Receptor-Mediated Transcriptional Regulation in Phase I, II, and III Xenobiotic Metabolizing Systems

TL;DR: The role of NRs was comprehensively drawn up in cholesterol and bile acid homeostasis in human hepatocyte and elucidate nuclear receptor mediated xenobiotic processing loops and direct future research.
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FANTOM5 CAGE profiles of human and mouse samples

Shuhei Noguchi, +185 more
- 29 Aug 2017 - 
TL;DR: In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE coupled with single-molecule sequencing to represent the consequence of transcriptional regulation in each analyzed state of mammalian cells.
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An empirical examination of the utility of codon-substitution models in phylogeny reconstruction.

TL;DR: Although computational burden makes codon models unfeasible for tree search in large data sets, it is suggested that they may be useful for comparing candidate trees and caution against use of overly complex substitution models.