Eiichiro Uchino

560 total citations
25 papers, 385 citations indexed

About

Eiichiro Uchino is a scholar working on Molecular Biology, Nephrology and Physiology. According to data from OpenAlex, Eiichiro Uchino has authored 25 papers receiving a total of 385 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 5 papers in Nephrology and 5 papers in Physiology. Recurrent topics in Eiichiro Uchino's work include Renal Diseases and Glomerulopathies (3 papers), Gut microbiota and health (3 papers) and Renal cell carcinoma treatment (2 papers). Eiichiro Uchino is often cited by papers focused on Renal Diseases and Glomerulopathies (3 papers), Gut microbiota and health (3 papers) and Renal cell carcinoma treatment (2 papers). Eiichiro Uchino collaborates with scholars based in Japan, United States and Singapore. Eiichiro Uchino's co-authors include Motoko Yanagita, Yasushi Okuno, Ryosuke Kojima, Koichi Murashita, Shigeyuki Nakaji, Shusuke Hiragi, Seiya Imoto, Takanori Hasegawa, Masanori Kakuta and Keiichi Kaneko and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Eiichiro Uchino

23 papers receiving 381 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Eiichiro Uchino Japan 10 142 98 58 56 50 25 385
Feiqian Wang China 9 268 1.9× 168 1.7× 24 0.4× 154 2.8× 27 0.5× 26 537
Jianzhong Sang China 8 139 1.0× 150 1.5× 47 0.8× 45 0.8× 152 3.0× 24 639
Steef Kurstjens Netherlands 12 34 0.2× 105 1.1× 25 0.4× 37 0.7× 59 1.2× 25 457
Lisa K. Torres United States 14 200 1.4× 86 0.9× 22 0.4× 25 0.4× 144 2.9× 24 718
Johan Frieling Netherlands 13 68 0.5× 13 0.1× 31 0.5× 18 0.3× 68 1.4× 20 447
Pika Meško Brguljan Slovenia 12 64 0.5× 114 1.2× 8 0.1× 141 2.5× 81 1.6× 23 487
Eliot Peyster United States 12 95 0.7× 44 0.4× 54 0.9× 24 0.4× 146 2.9× 27 629
Andrzej Tomasik Poland 12 61 0.4× 19 0.2× 15 0.3× 43 0.8× 50 1.0× 61 485
Giulia Ligabue Italy 13 104 0.7× 168 1.7× 22 0.4× 37 0.7× 60 1.2× 59 506
Marcus A. Urey United States 7 119 0.8× 40 0.4× 47 0.8× 45 0.8× 26 0.5× 32 458

Countries citing papers authored by Eiichiro Uchino

Since Specialization
Citations

This map shows the geographic impact of Eiichiro Uchino'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 Eiichiro Uchino with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eiichiro Uchino more than expected).

Fields of papers citing papers by Eiichiro Uchino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eiichiro Uchino. 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 Eiichiro Uchino. The network helps show where Eiichiro Uchino may publish in the future.

Co-authorship network of co-authors of Eiichiro Uchino

This figure shows the co-authorship network connecting the top 25 collaborators of Eiichiro Uchino. A scholar is included among the top collaborators of Eiichiro Uchino based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Eiichiro Uchino. Eiichiro Uchino is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fujimoto, Kenji, Eiichiro Uchino, Ken Itoh, et al.. (2025). Network analysis reveals causal relationships among individual background risk factors leading to influenza susceptibility. Scientific Reports. 15(1). 30721–30721.
2.
Nishikawa, Yoshitaka, Takahiro Horimatsu, Eiichiro Uchino, et al.. (2024). Proteinuria frequency and subsequent renal dysfunction in bevacizumab-treated patients: a single center, retrospective, observational study. International Journal of Clinical Oncology. 29(4). 398–406. 2 indexed citations
3.
Uchino, Eiichiro, Hirofumi Suzuki, T. Takagi, et al.. (2024). Subgrouping Causal Networks of Disease Onset in Large-scale Health and Medical Data using Supercomputer Fugaku. 4384–4391.
4.
Nakamura, Kazuki, Eiichiro Uchino, Noriaki Sato, et al.. (2023). Individual health-disease phase diagrams for disease prevention based on machine learning. Journal of Biomedical Informatics. 144. 104448–104448. 5 indexed citations
5.
Kaneko, Keiichi, Yuki Sato, Eiichiro Uchino, et al.. (2022). Lineage tracing analysis defines erythropoietin-producing cells as a distinct subpopulation of resident fibroblasts with unique behaviors. Kidney International. 102(2). 280–292. 19 indexed citations
6.
Nakagawa, Shunsaku, Daiki Hira, Satoshi Imai, et al.. (2022). Use of proton pump inhibitors and macrolide antibiotics and risk of acute kidney injury: a self-controlled case series study. BMC Nephrology. 23(1). 383–383. 9 indexed citations
7.
Nakamura, Kazuki, Ryosuke Kojima, Eiichiro Uchino, et al.. (2021). Health improvement framework for actionable treatment planning using a surrogate Bayesian model. Nature Communications. 12(1). 3088–3088. 8 indexed citations
8.
Uchino, Eiichiro, Ryosuke Kojima, Shusuke Hiragi, et al.. (2021). Evaluation of Kidney Histological Images Using Unsupervised Deep Learning. Kidney International Reports. 6(9). 2445–2454. 13 indexed citations
9.
Yamamoto, Shinya, Masamichi Yamamoto, Jin Nakamura, et al.. (2020). Spatiotemporal ATP Dynamics during AKI Predict Renal Prognosis. Journal of the American Society of Nephrology. 31(12). 2855–2869. 37 indexed citations
10.
Kakuta, Masanori, Takanori Hasegawa, Rui Yamaguchi, et al.. (2020). Metagenomic analysis of bacterial species in tongue microbiome of current and never smokers. npj Biofilms and Microbiomes. 6(1). 39 indexed citations
11.
Kakuta, Masanori, Takanori Hasegawa, Rui Yamaguchi, et al.. (2020). Metagenomic profiling of gut microbiome in early chronic kidney disease. Nephrology Dialysis Transplantation. 36(9). 1675–1684. 44 indexed citations
12.
13.
Uchino, Eiichiro, Kanata Suzuki, Ryosuke Kojima, et al.. (2020). Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach. International Journal of Medical Informatics. 141. 104231–104231. 59 indexed citations
14.
Kakuta, Masanori, Eiichiro Uchino, Takanori Hasegawa, et al.. (2020). The relationship between cigarette smoking and the tongue microbiome in an East Asian population. Journal of Oral Microbiology. 12(1). 1742527–1742527. 28 indexed citations
15.
Kamada, Mayumi, Masahiko Nakatsui, Ryosuke Kojima, et al.. (2019). MGeND: an integrated database for Japanese clinical and genomic information. Human Genome Variation. 6(1). 53–53. 10 indexed citations
16.
Uchino, Eiichiro, Daisuke Takada, Haruta Mogami, et al.. (2018). Membranous nephropathy associated with pregnancy: an anti-phospholipase A2 receptor antibody-positive case report. CEN Case Reports. 7(1). 101–106. 4 indexed citations
17.
Uchino, Eiichiro, Keita Mori, Hideki Yokoi, et al.. (2018). Peritonitis due to Mycobacterium abscessus in peritoneal dialysis patients: case presentation and mini-review. Renal Replacement Therapy. 4(1). 5 indexed citations
18.
19.
Yokoi, Hideki, Hirotaka Imamaki, Eiichiro Uchino, et al.. (2017). Renal-limited vasculitis with elevated levels of multiple antibodies. CEN Case Reports. 6(1). 79–84. 6 indexed citations
20.
Uchino, Eiichiro, Naoya Kondo, Takeshi Matsubara, & Motoko Yanagita. (2016). Automated Electronic Alert Systems for Acute Kidney Injury: Current Status and Future Perspectives. Contributions to nephrology. 189. 124–129. 5 indexed citations

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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