Seiichi Uchimura

448 total citations
10 papers, 323 citations indexed

About

Seiichi Uchimura is a scholar working on Cell Biology, Molecular Biology and Plant Science. According to data from OpenAlex, Seiichi Uchimura has authored 10 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cell Biology, 8 papers in Molecular Biology and 2 papers in Plant Science. Recurrent topics in Seiichi Uchimura's work include Microtubule and mitosis dynamics (8 papers), Cellular transport and secretion (4 papers) and Photosynthetic Processes and Mechanisms (3 papers). Seiichi Uchimura is often cited by papers focused on Microtubule and mitosis dynamics (8 papers), Cellular transport and secretion (4 papers) and Photosynthetic Processes and Mechanisms (3 papers). Seiichi Uchimura collaborates with scholars based in Japan, Switzerland and France. Seiichi Uchimura's co-authors include Etsuko Muto, Yusuke Oguchi, Shin’ichi Ishiwata, Itsushi Minoura, Yoshihiko Yamakita, Sergey V. Mikhailenko, Takashi Ohki, Jun‐ichi Nikawa, Takeo Usui and Miho Katsuki and has published in prestigious journals such as Nature Communications, The Journal of Cell Biology and The EMBO Journal.

In The Last Decade

Seiichi Uchimura

10 papers receiving 322 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seiichi Uchimura Japan	8	275	242	31	19	11	10	323
Melissa C. Pamula United States	6	194 0.7×	227 0.9×	26 0.8×	13 0.7×	5 0.5×	8	282
Eva Karasmanis United States	7	201 0.7×	220 0.9×	10 0.3×	23 1.2×	5 0.5×	13	315
Annapurna Vemu United States	6	314 1.1×	309 1.3×	20 0.6×	30 1.6×	8 0.7×	9	405
N. A. Shanina Russia	11	248 0.9×	282 1.2×	43 1.4×	22 1.2×	3 0.3×	18	381
Tien-chen Lin Germany	8	337 1.2×	360 1.5×	44 1.4×	47 2.5×	13 1.2×	9	437
Laurent Chesneau France	6	336 1.2×	244 1.0×	32 1.0×	16 0.8×	14 1.3×	10	413
Mie Wong Switzerland	5	225 0.8×	202 0.8×	10 0.3×	18 0.9×	5 0.5×	7	306
Elisabeth A. Geyer United States	10	434 1.6×	403 1.7×	59 1.9×	4 0.2×	4 0.4×	13	483
Natalya Pashkova United States	10	216 0.8×	363 1.5×	28 0.9×	55 2.9×	4 0.4×	14	459
Andrew T. Mackey United States	9	340 1.2×	341 1.4×	50 1.6×	11 0.6×	3 0.3×	9	469

Countries citing papers authored by Seiichi Uchimura

Since Specialization

Citations

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

Fields of papers citing papers by Seiichi Uchimura

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seiichi Uchimura

This figure shows the co-authorship network connecting the top 25 collaborators of Seiichi Uchimura. A scholar is included among the top collaborators of Seiichi Uchimura 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 Seiichi Uchimura. Seiichi Uchimura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Kumada, Yoichi, et al.. (2022). Strategies for selection and identification of rabbit single-chain Fv antibodies as ligand in affinity chromatography. Journal of Bioscience and Bioengineering. 134(3). 233–239. 3 indexed citations

Kamimura, Shinji, Masahito Hayashi, Kien Xuan Ngo, et al.. (2021). GTP-dependent formation of straight tubulin oligomers leads to microtubule nucleation. The Journal of Cell Biology. 220(4). 26 indexed citations

Minoura, Itsushi, et al.. (2016). Reversal of axonal growth defects in an extraocular fibrosis model by engineering the kinesin–microtubule interface. Nature Communications. 7(1). 10058–10058. 25 indexed citations

Hotta, Takashi, Satoshi Fujita, Seiichi Uchimura, et al.. (2016). Affinity Purification and Characterization of Functional Tubulin from Cell Suspension Cultures of Arabidopsis and Tobacco. PLANT PHYSIOLOGY. 170(3). 1189–1205. 22 indexed citations

Uchimura, Seiichi, Takashi Fujii, Itsushi Minoura, et al.. (2015). A flipped ion pair at the dynein–microtubule interface is critical for dynein motility and ATPase activation. The Journal of Cell Biology. 208(2). 211–222. 33 indexed citations

Minoura, Itsushi, et al.. (2013). Overexpression, purification, and functional analysis of recombinant human tubulin dimer. FEBS Letters. 587(21). 3450–3455. 69 indexed citations

Oguchi, Yusuke, Seiichi Uchimura, Takashi Ohki, Sergey V. Mikhailenko, & Shin’ichi Ishiwata. (2011). The bidirectional depolymerizer MCAK generates force by disassembling both microtubule ends. Nature Cell Biology. 13(7). 846–852. 47 indexed citations

Uchimura, Seiichi, et al.. (2010). Key residues on microtubule responsible for activation of kinesin ATPase. The EMBO Journal. 29(7). 1167–1175. 41 indexed citations

Uchimura, Seiichi, Yusuke Oguchi, Miho Katsuki, et al.. (2006). Identification of a strong binding site for kinesin on the microtubule using mutant analysis of tubulin. The EMBO Journal. 25(24). 5932–5941. 52 indexed citations

10.

Uchimura, Seiichi, Minetaka Sugiyama, & Jun‐ichi Nikawa. (2005). Effects of N-Glycosylation and Inositol on the ER Stress Response in YeastSaccharomyces cerevisiae. Bioscience Biotechnology and Biochemistry. 69(7). 1274–1280. 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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