Keiichi Koizumi

4.1k total citations
116 papers, 3.3k citations indexed

About

Keiichi Koizumi is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Keiichi Koizumi has authored 116 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Biology, 45 papers in Oncology and 29 papers in Immunology. Recurrent topics in Keiichi Koizumi's work include Angiogenesis and VEGF in Cancer (16 papers), Chemokine receptors and signaling (14 papers) and Lymphatic System and Diseases (12 papers). Keiichi Koizumi is often cited by papers focused on Angiogenesis and VEGF in Cancer (16 papers), Chemokine receptors and signaling (14 papers) and Lymphatic System and Diseases (12 papers). Keiichi Koizumi collaborates with scholars based in Japan, Thailand and United States. Keiichi Koizumi's co-authors include Ikuo Saiki, Hiroaki Sakurai, Yurika Saitoh, Takuya Akashi, Kazuo Yasumoto, Shozo Hojo, T. Minami, Takashi Nakayama, Osamu Yoshie and Min‐Kyung Choo and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and The Journal of Immunology.

In The Last Decade

Keiichi Koizumi

111 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Keiichi Koizumi Japan 31 1.5k 1.3k 781 477 243 116 3.3k
Keigo Nishida Japan 31 2.0k 1.3× 824 0.6× 1.4k 1.8× 371 0.8× 159 0.7× 73 4.5k
B. Mark Woerner United States 20 1.3k 0.8× 1.1k 0.8× 527 0.7× 711 1.5× 108 0.4× 21 4.8k
William L. Blalock Italy 30 2.9k 2.0× 931 0.7× 497 0.6× 699 1.5× 127 0.5× 73 4.3k
Fredika M. Robertson United States 36 1.3k 0.9× 1.2k 0.9× 468 0.6× 957 2.0× 117 0.5× 89 3.9k
Tohru Kamata Japan 31 2.7k 1.8× 881 0.7× 1.2k 1.5× 495 1.0× 257 1.1× 74 4.6k
Joan Gil Spain 38 2.9k 1.9× 918 0.7× 557 0.7× 509 1.1× 226 0.9× 110 4.9k
Fumin Chang United States 17 2.3k 1.5× 964 0.7× 382 0.5× 527 1.1× 91 0.4× 26 3.5k
Kevin Gardner United States 31 2.1k 1.4× 649 0.5× 549 0.7× 592 1.2× 197 0.8× 101 3.6k
Amy M. Fulton United States 40 1.2k 0.8× 1.8k 1.3× 1.8k 2.3× 754 1.6× 90 0.4× 95 4.7k
Matteo Parri Italy 29 1.7k 1.1× 641 0.5× 408 0.5× 724 1.5× 363 1.5× 59 3.0k

Countries citing papers authored by Keiichi Koizumi

Since Specialization
Citations

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

Fields of papers citing papers by Keiichi Koizumi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Keiichi Koizumi

This figure shows the co-authorship network connecting the top 25 collaborators of Keiichi Koizumi. A scholar is included among the top collaborators of Keiichi Koizumi 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 Keiichi Koizumi. Keiichi Koizumi 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.
Akagi, Kazutaka, Yinji Jin, Keiichi Koizumi, et al.. (2025). Integration of Dynamical Network Biomarkers, Control Theory and Drosophila Model Identifies Vasa/DDX4 as the Potential Therapeutic Targets for Metabolic Syndrome. Cells. 14(6). 415–415. 1 indexed citations
2.
Nguyen, Khanh Cong, Yue Zhou, Satoru Yokoyama, et al.. (2025). Allosteric SHP2 inhibitors suppress lung cancer cell migration by inhibiting non-canonical activation of EphA2 via the ERK-RSK signaling pathway. Scientific Reports. 15(1). 36926–36926.
3.
Fujita, Hideaki, Kazuhiro Sudo, Yukio Nakamura, et al.. (2024). Yuragi biomarker concept for evaluating human induced pluripotent stem cells using heterogeneity-based Raman finger-printing. Biophysics and Physicobiology. 21(Supplemental). n/a–n/a. 2 indexed citations
4.
Koizumi, Keiichi, Yusuke Oshima, Akinori Wada, et al.. (2024). Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis. International Journal of Molecular Sciences. 25(3). 1570–1570. 5 indexed citations
5.
Okada, Takuya, Ryusuke Sawada, Yoshihiro Yamanishi, et al.. (2023). Design and structural optimization of thiadiazole derivatives with potent GLS1 inhibitory activity. Bioorganic & Medicinal Chemistry Letters. 93. 129438–129438. 2 indexed citations
6.
Oshima, Yusuke, et al.. (2023). Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy. International Journal of Molecular Sciences. 24(15). 12170–12170. 7 indexed citations
7.
Akagi, Kazutaka, Keiichi Koizumi, Makoto Kadowaki, Isao Kitajima, & Shigeru Saito. (2023). New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory. Cells. 12(18). 2297–2297. 2 indexed citations
8.
Omata, Daiki, et al.. (2022). Feasibility Study of Novel Nanoparticles Derived from Glycyrrhizae Radix as Vaccine Adjuvant for Cancer Immunotherapy. Immunotherapy. 14(18). 1443–1455. 2 indexed citations
9.
Koizumi, Keiichi, et al.. (2013). Shikonin inhibits lymphangiogenesis in vitro via the modulation of cell adhesion. 30(4). 176–182. 1 indexed citations
10.
Saiki, Ikuo, Keiichi Koizumi, Hirozo Goto, et al.. (2013). The Long-Term Effects of a Kampo Medicine, Juzentaihoto, on Maintenance of Antibody Titer in Elderly People after Influenza Vaccination. Evidence-based Complementary and Alternative Medicine. 2013. 1–8. 15 indexed citations
11.
Oka, Hiroshi, Hirozo Goto, Keiichi Koizumi, et al.. (2011). Cinnamaldehyde and paeonol increase HIF-1α activity in proximal tubular epithelial cells under hypoxia. 28(3). 149–157. 1 indexed citations
12.
13.
Koizumi, Keiichi, et al.. (2011). eNOS and Hsp90 Interaction Directly Correlates with Cord Formation in Human Lymphatic Endothelial Cells. Lymphatic Research and Biology. 9(1). 53–59. 7 indexed citations
14.
Koizumi, Keiichi, et al.. (2010). Methanol extract of Polygonati Rhizoma enhances the tube formation of rat lymphatic endothelial cells. 27(2). 59–65. 2 indexed citations
15.
Koizumi, Keiichi, T. Minami, Shunsuke Suzuki, et al.. (2009). Inducible Capillary Formation in Lymphatic Endothelial Cells by Blocking Lipid Phosphate Phosphatase-3 Activity. Lymphatic Research and Biology. 7(2). 69–74. 8 indexed citations
16.
Koizumi, Keiichi, Yurika Saitoh, T. Minami, et al.. (2009). Role of CX3CL1/Fractalkine in Osteoclast Differentiation and Bone Resorption. The Journal of Immunology. 183(12). 7825–7831. 115 indexed citations
17.
18.
Koizumi, Keiichi, et al.. (2003). Anti-tumor angiogenic effect of a matrix metalloproteinase inhibitor MMI270.. PubMed. 23(1A). 411–7. 12 indexed citations
19.
Tsunoda, Shin‐ichi, Iwao Ohizumi, Junji Matsui, et al.. (1999). Specific binding of TES-23 antibody to tumour vascular endothelium in mice, rats and human cancer tissue: A novel drug carrier for cancer targeting therapy. British Journal of Cancer. 81(7). 1155–1161. 12 indexed citations
20.
Koizumi, Keiichi, et al.. (1991). Induction of 31,000 protein by PGD2-treated vascular endothelial cells.. PubMed. 21B. 891–4. 1 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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