Jun Agata

1.7k citations
29 papers · 1.5k · h-index 20

Impact in

Papers in

Jun Agata

29 papers receiving 1.4k citations

Peers

Jun Agata
Comparison fields: 5 of 79
  • Genetics 248
  • Cardiology and Cardiovascular Medicine 501
  • Endocrine and Autonomic Systems 80
  • Nephrology 82
  • Cellular and Molecular Neuroscience 111
Replace Atsushi Kosaki with:
Atsushi Kosaki Japan
Hanna K. Leskinen Finland
Shabana Din United States
Li Lin China
Ben He China
D. A. S. G. Mary United Kingdom
Megan C. Sherwood United States
Issei Tachibana Japan
István Szokodi Hungary
Giuseppina Milano Switzerland
Jun Agata relative to Atsushi Kosaki Japan Atsushi Kosaki's profile →
Citations per field
00.5×1.5×2.1×
Atsushi Kosaki · 1×
Citations per year

Countries citing papers authored by Jun Agata

Since Specialization
Citations

This map shows the geographic impact of Jun Agata's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Agata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Agata more than expected).

Fields of papers citing papers by Jun Agata

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Agata. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Agata. The network helps show where Jun Agata may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Agata, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Agata Line = papers co-authored together Jun Agata links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1997201
2 2002147
3 2006115
4 2003113
5 200280
6 200279
7 200575
8 200070
9 200567
10 200260
11 200052
12 200252
13 200445
14 201945
15 200144
16 199528
17 199924
18 200023
19 200422
20 199821

About Jun Agata

Jun Agata is a scholar working on Genetics, Cardiology and Cardiovascular Medicine, Endocrinology, Diabetes and Metabolism, Cellular and Molecular Neuroscience and Endocrine and Autonomic Systems, having authored 29 papers that have together received 1.5k indexed citations. Recurring topics across this work include Coagulation, Bradykinin, Polyphosphates, and Angioedema (8 papers), Renin-Angiotensin System Studies (4 papers), Atrial Fibrillation Management and Outcomes (3 papers), Neuropeptides and Animal Physiology (3 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (3 papers), Cardiac Fibrosis and Remodeling (3 papers), Natural Antidiabetic Agents Studies (2 papers) and Angiogenesis and VEGF in Cancer (2 papers). The work is most often cited by research in Genetics (248 citations), Cardiology and Cardiovascular Medicine (501 citations), Endocrine and Autonomic Systems (80 citations), Nephrology (82 citations) and Cellular and Molecular Neuroscience (111 citations). Jun Agata has collaborated with scholars based in United States, Japan and United Kingdom. Frequent co-authors include Lee Chao, Julie Chao, Hideaki Yoshida, Qing Miao, Hang Hubert Yin, Nobuyuki Ura, ROBERT SCOTT SMITH, Kazuaki Shimamoto, Eric Dobrzynski and Kazuo Kato. Their work appears in journals such as Hypertension Research, Arteriosclerosis Thrombosis and Vascular Biology, American Journal of Hypertension, Hypertension and Blood.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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