Koji Eguchi
Overview
Explore the profile of Koji Eguchi including associated specialties, affiliations and a list of published articles.
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7
Citations
18
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Recent Articles
1.
Xu W, Eguchi K
PLoS One
. 2022 Nov;
17(11):e0277104.
PMID: 36331905
We propose rTopicVec, a supervised topic embedding model that predicts response variables associated with documents by analyzing the text data. Topic modeling leverages document-level word co-occurrence patterns to learn latent...
2.
Hiramatsu A, Izumi Y, Eguchi K, Matsuo N, Deng Q, Inoue H, et al.
Hypertension
. 2021 Oct;
78(5):1335-1346.
PMID: 34601973
[Figure: see text].
3.
Eguchi K, Izumi Y, Yasuoka Y, Nakagawa T, Ono M, Maruyama K, et al.
J Endocrinol
. 2021 Mar;
249(2):95-112.
PMID: 33705345
Rhesus C glycoprotein (Rhcg), an ammonia transporter, is a key molecule in urinary acid excretion and is expressed mainly in the intercalated cells (ICs) of the renal collecting duct. In...
4.
Eguchi K, Izumi Y, Nakayama Y, Inoue H, Marume T, Matsuo N, et al.
Nephrology (Carlton)
. 2018 Dec;
24(11):1131-1141.
PMID: 30582257
Aim: Metabolic acidosis occurs due to insufficient urinary ammonium excretion as chronic kidney disease (CKD) advances. Because obese subjects tend to have excessive consumption of protein and sodium chloride, they...
5.
Izumi Y, Inoue H, Nakayama Y, Eguchi K, Yasuoka Y, Matsuo N, et al.
PLoS One
. 2017 Sep;
12(8):e0184185.
PMID: 28859164
Metabolic acidosis often results from chronic kidney disease; in turn, metabolic acidosis accelerates the progression of kidney injury. The mechanisms for how acidosis facilitates kidney injury are not fully understood....
6.
DuMond J, Ramkissoon K, Zhang X, Izumi Y, Wang X, Eguchi K, et al.
Physiol Genomics
. 2016 Jan;
48(4):290-305.
PMID: 26757802
NFAT5 is an osmoregulated transcription factor that particularly increases expression of genes involved in protection against hypertonicity. Transcription factors often contain unstructured regions that bind co-regulatory proteins that are crucial...
7.
Aso T, Eguchi K
Genome Inform
. 2010 Feb;
23(1):3-12.
PMID: 20180257
This paper investigates applying statistical topic models to extract and predict relationships between biological entities, especially protein mentions. A statistical topic model, Latent Dirichlet Allocation (LDA) is promising; however, it...