H
Hiroki R. Ueda
Researcher at University of Tokyo
Publications - 247
Citations - 21711
Hiroki R. Ueda is an academic researcher from University of Tokyo. The author has contributed to research in topics: Circadian clock & Circadian rhythm. The author has an hindex of 59, co-authored 211 publications receiving 18300 citations. Previous affiliations of Hiroki R. Ueda include Intec, Inc. & Osaka University.
Papers
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Journal ArticleDOI
NEK9 regulates primary cilia formation by acting as a selective autophagy adaptor for MYH9/myosin IIA.
Yasuhiro Yamamoto,Haruka Chino,Satoshi Tsukamoto,Koji L. Ode,Hiroki R. Ueda,Noboru Mizushima +5 more
TL;DR: In this article, the authors identify NIMA-related kinase 9 (NEK9) as a GABARAP-interacting protein and find that NEK9 and its LC3interacting region (LIR) are required for primary cilia formation.
Journal ArticleDOI
Systems biology of mammalian circadian clocks
TL;DR: This study captures some of the defining features of PTSD in human patients using an animal model wherein a single episode of acute stress causes higher anxiety and spinogenesis in the amygdala not one, but ten days later.
Journal Article
Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging
TL;DR: A protocol for advanced CUBIC (Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis) is described, which provides a platform for organism-level systems biology by comprehensively detecting cells in a whole organ or body.
Journal ArticleDOI
Ubiquitination of phosphatidylethanolamine in organellar membranes.
Jun-ichi Sakamaki,Koji L. Ode,Yoshitaka Kurikawa,Hiroki R. Ueda,Hayashi Yamamoto,Noboru Mizushima +5 more
TL;DR: In this article , the ubiquitin-like NEDD8 and ISG15 are also conjugated to membrane phospholipids, mainly phosphatidylethanolamine (PE), in yeast and mammalian cells.
Posted ContentDOI
nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification
TL;DR: A new software, called nanoDoc, is presented, for detecting PTMs from DRS data using a deep neural network and a tentative classification of PTMs using unsupervised clustering is demonstrated.