Takashi Kawase

2.4k total citations
18 papers, 542 citations indexed

About

Takashi Kawase is a scholar working on Molecular Biology, Plant Science and Cellular and Molecular Neuroscience. According to data from OpenAlex, Takashi Kawase has authored 18 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 4 papers in Plant Science and 3 papers in Cellular and Molecular Neuroscience. Recurrent topics in Takashi Kawase's work include Advanced biosensing and bioanalysis techniques (4 papers), Single-cell and spatial transcriptomics (3 papers) and Neurobiology and Insect Physiology Research (3 papers). Takashi Kawase is often cited by papers focused on Advanced biosensing and bioanalysis techniques (4 papers), Single-cell and spatial transcriptomics (3 papers) and Neurobiology and Insect Physiology Research (3 papers). Takashi Kawase collaborates with scholars based in Japan, United States and Switzerland. Takashi Kawase's co-authors include Ikuko Hara‐Nishimura, Hiroshi Sugiyama, Toshikazu Bando, Shuji Kaieda, Hidetoshi Tahara, Kenichi Tanaka, Ken‐ichi Shinohara, Yan Xu, Yuta Sannohe and Hideji Osuga and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Takashi Kawase

18 papers receiving 531 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Takashi Kawase Japan 11 281 131 94 71 58 18 542
Simon P. Pearce United Kingdom 17 380 1.4× 422 3.2× 25 0.3× 122 1.7× 44 0.8× 21 886
Marco Vilela United States 15 420 1.5× 110 0.8× 10 0.1× 121 1.7× 243 4.2× 20 806
Lingxin Kong China 19 375 1.3× 61 0.5× 110 1.2× 26 0.4× 199 3.4× 55 972
Yoshiro Hanyu Japan 13 349 1.2× 24 0.2× 6 0.1× 52 0.7× 72 1.2× 39 682
Xuefei Zhong United States 22 599 2.1× 68 0.5× 69 0.7× 48 0.7× 128 2.2× 50 1.3k
Yoon Sup Choi South Korea 9 632 2.2× 112 0.9× 12 0.1× 121 1.7× 73 1.3× 12 870
Wenze Li United States 14 294 1.0× 138 1.1× 4 0.0× 56 0.8× 190 3.3× 45 909
Paul J. Michalski United States 13 226 0.8× 38 0.3× 6 0.1× 109 1.5× 31 0.5× 19 483
Jong‐Myoung Kim South Korea 14 604 2.1× 34 0.3× 13 0.1× 30 0.4× 398 6.9× 53 1.3k
Bin Huang China 15 480 1.7× 180 1.4× 27 0.3× 37 0.5× 249 4.3× 61 694

Countries citing papers authored by Takashi Kawase

Since Specialization
Citations

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

Fields of papers citing papers by Takashi Kawase

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takashi Kawase

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

All Works

18 of 18 papers shown
1.
Gandin, Valentina, Liang-Zhong Yang, Takashi Kawase, et al.. (2025). Deep-tissue transcriptomics and subcellular imaging at high spatial resolution. Science. 388(6744). eadq2084–eadq2084. 12 indexed citations
2.
Clements, Jody, Cristian Goina, Philip M. Hubbard, et al.. (2024). NeuronBridge: an intuitive web application for neuronal morphology search across large data sets. BMC Bioinformatics. 25(1). 114–114. 13 indexed citations
3.
Dong, Peng, Shu Zhang, Valentina Gandin, et al.. (2024). Cohesin prevents cross-domain gene coactivation. Nature Genetics. 56(8). 1654–1664. 7 indexed citations
4.
Lillvis, Joshua L., Hideo Otsuna, Takashi Kawase, et al.. (2022). Rapid reconstruction of neural circuits using tissue expansion and light sheet microscopy. eLife. 11. 25 indexed citations
5.
Kita, Yoshiaki, Yan Wang, Tsutomu Hashikawa, et al.. (2021). Cellular-resolution gene expression profiling in the neonatal marmoset brain reveals dynamic species- and region-specific differences. Proceedings of the National Academy of Sciences. 118(18). 19 indexed citations
6.
Yang, Ching-Po, Rosa Linda Miyares, Yu-Fen Huang, et al.. (2020). Conservation and divergence of related neuronal lineages in the Drosophila central brain. eLife. 9. 21 indexed citations
7.
Sugano, Shigeo S., Takashi Kawase, Makoto Shirakawa, et al.. (2017). The chemical compound bubblin induces stomatal mispatterning in Arabidopsis by disrupting the intrinsic polarity of stomatal lineage cells. Development. 144(3). 499–506. 17 indexed citations
8.
Kashiwazaki, Gengo, Rina Maeda, Takashi Kawase, et al.. (2017). Evaluation of alkylating pyrrole-imidazole polyamide conjugates by a novel method for high-throughput sequencer. Bioorganic & Medicinal Chemistry. 26(1). 1–7. 6 indexed citations
9.
Kashiwazaki, Gengo, Anandhakumar Chandran, Sefan Asamitsu, et al.. (2016). Comparative Analysis of DNA‐Binding Selectivity of Hairpin and Cyclic Pyrrole‐Imidazole Polyamides Based on Next‐Generation Sequencing. ChemBioChem. 17(18). 1752–1758. 9 indexed citations
10.
Kawase, Takashi, Shigeo S. Sugano, Tomoo Shimada, & Ikuko Hara‐Nishimura. (2016). Differential and Simultaneous Visualization of Cells and Airspaces in Plant Leaves. BIO-PROTOCOL. 6(11). 1 indexed citations
11.
Hamada, Takahiro, Haruko Ueda, Takashi Kawase, & Ikuko Hara‐Nishimura. (2014). Microtubules Contribute to Tubule Elongation and Anchoring of Endoplasmic Reticulum, Resulting in High Network Complexity in Arabidopsis    . PLANT PHYSIOLOGY. 166(4). 1869–1876. 54 indexed citations
12.
Kawase, Takashi, Shigeo S. Sugano, Tomoo Shimada, & Ikuko Hara‐Nishimura. (2014). A direction‐selective local‐thresholding method, DSLT, in combination with a dye‐based method for automated three‐dimensional segmentation of cells and airspaces in developing leaves. The Plant Journal. 81(2). 357–366. 9 indexed citations
13.
Yamaoka, Shohei, Makoto Shirakawa, Yoichiro Fukao, et al.. (2013). Identification and Dynamics of Arabidopsis Adaptor Protein-2 Complex and Its Involvement in Floral Organ Development. The Plant Cell. 25(8). 2958–2969. 101 indexed citations
14.
Shinohara, Ken‐ichi, Yuta Sannohe, Shuji Kaieda, et al.. (2010). A Chiral Wedge Molecule Inhibits Telomerase Activity. Journal of the American Chemical Society. 132(11). 3778–3782. 172 indexed citations
15.
16.
Yamamoto, Kazuhiro, et al.. (2000). Hot strip mill tension–looper control based on decentralization and coordination. Control Engineering Practice. 8(3). 337–344. 37 indexed citations
17.
Yamamoto, Kazuhiro, et al.. (1998). Hot Strip Mill Tension-Looper Control Based on Decentralization and Coordination. IFAC Proceedings Volumes. 31(23). 99–104. 1 indexed citations
18.
Kawase, Takashi, Hideaki Sakai, & H. Tokumaru. (1983). Recursive least squares circular lattice and escalator estimation algorithms. IEEE Transactions on Acoustics Speech and Signal Processing. 31(1). 228–231. 33 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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