Toshie Kai

4.4k total citations · 1 hit paper
40 papers, 3.3k citations indexed

About

Toshie Kai is a scholar working on Molecular Biology, Plant Science and Genetics. According to data from OpenAlex, Toshie Kai has authored 40 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 21 papers in Plant Science and 9 papers in Genetics. Recurrent topics in Toshie Kai's work include Chromosomal and Genetic Variations (20 papers), CRISPR and Genetic Engineering (11 papers) and RNA Research and Splicing (7 papers). Toshie Kai is often cited by papers focused on Chromosomal and Genetic Variations (20 papers), CRISPR and Genetic Engineering (11 papers) and RNA Research and Splicing (7 papers). Toshie Kai collaborates with scholars based in Singapore, Japan and United States. Toshie Kai's co-authors include Allan C. Spradling, Daniela Drummond‐Barbosa, Ai Khim Lim, Jun Wei Pek, Amit Anand, Veena S. Patil, Benjamin Ohlstein, Tetsuro Yonesaki, Harold E. Selick and Chiemi Nishimiya‐Fujisawa and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Toshie Kai

39 papers receiving 3.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Stem cells find their niche

2001 1.1k citations Allan C. Spradling, Daniela Drummond‐Barbosa et al. Nature profile →

Peers

Toshie Kai

MB

PS

GENET

ONCOL

IMMUN

Francesc López‐Giráldez United States

Christian Schöfer Austria

Hannele Ruohola‐Baker United States

Daniela Drummond‐Barbosa United States

Tom Strachan United Kingdom

Rui Yi United States

Walter Knöchel Germany

Luc Leyns Belgium

Ruchi Bajpai United States

Masanori Taira Japan

Francesc López‐Giráldez United States

Toshie Kai

1.2k ×0.5

MB

754 ×0.9

PS

368 ×0.7

GENET

267 ×0.9

ONCOL

324 ×1.1

IMMUN

Citations per year, relative to Toshie Kai Toshie Kai (= 1×) peers Francesc López‐Giráldez

Countries citing papers authored by Toshie Kai

Since Specialization

Citations

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

Fields of papers citing papers by Toshie Kai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Toshie Kai

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

All Works

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20 of 20 papers shown

1.

Kawaguchi, Shinichi, et al.. (2024). In silico screening by AlphaFold2 program revealed the potential binding partners of nuage-localizing proteins and piRNA-related proteins. eLife. 13.

2.

Suyama, Ritsuko & Toshie Kai. (2024). piRNA processing within non‐membrane structures is governed by constituent proteins and their functional motifs. FEBS Journal. 292(11). 2715–2736. 3 indexed citations

3.

Iki, Taichiro, Shinichi Kawaguchi, & Toshie Kai. (2023). miRNA/siRNA-directed pathway to produce noncoding piRNAs from endogenous protein-coding regions ensures Drosophila spermatogenesis. Science Advances. 9(29). eadh0397–eadh0397. 8 indexed citations

4.

Arakawa, Kazuharu, Tetsuro Hirose, Toshifumi Inada, et al.. (2023). Nondomain biopolymers: Flexible molecular strategies to acquire biological functions. Genes to Cells. 28(8). 539–552. 6 indexed citations

5.

Suyama, Ritsuko, et al.. (2023). Tejas functions as a core component in nuage assembly and precursor processing in Drosophila piRNA biogenesis. The Journal of Cell Biology. 222(10). 6 indexed citations

6.

Suyama, Ritsuko, et al.. (2023). Microbes control Drosophila germline stem cell increase and egg maturation through hormonal pathways. Communications Biology. 6(1). 1287–1287. 2 indexed citations

7.

Iki, Taichiro, et al.. (2022). The Tudor Domain-Containing Protein, Kotsubu (CG9925), Localizes to the Nuage and Functions in piRNA Biogenesis in D. melanogaster. Frontiers in Molecular Biosciences. 9. 818302–818302. 9 indexed citations

8.

Kawaguchi, Shinichi, et al.. (2020). Drosophila MARF1 ensures proper oocyte maturation by regulating nanos expression. PLoS ONE. 15(4). e0231114–e0231114. 9 indexed citations

9.

Iki, Taichiro, et al.. (2020). Modulation of Ago2 Loading by Cyclophilin 40 Endows a Unique Repertoire of Functional miRNAs during Sperm Maturation in Drosophila. Cell Reports. 33(6). 108380–108380. 7 indexed citations

10.

Anand, Amit, et al.. (2016). The piRNA pathway is developmentally regulated during spermatogenesis in Drosophila. RNA. 22(7). 1044–1054. 37 indexed citations

11.

Pek, Jun Wei, Veena S. Patil, & Toshie Kai. (2012). piRNA pathway and the potential processing site, the nuage, in the Drosophila germline. Development Growth & Differentiation. 54(1). 66–77. 37 indexed citations

12.

Pek, Jun Wei & Toshie Kai. (2010). A Role for Vasa in Regulating Mitotic Chromosome Condensation in Drosophila. Current Biology. 21(1). 39–44. 61 indexed citations

13.

Pek, Jun Wei, Ai Khim Lim, & Toshie Kai. (2009). Drosophila Maelstrom Ensures Proper Germline Stem Cell Lineage Differentiation by Repressing microRNA-7. Developmental Cell. 17(3). 417–424. 93 indexed citations

14.

Lim, Ai Khim & Toshie Kai. (2007). Unique germ-line organelle, nuage, functions to repress selfish genetic elements in Drosophila melanogaster. Proceedings of the National Academy of Sciences. 104(16). 6714–6719. 299 indexed citations

15.

Kai, Toshie, et al.. (2005). The expression profile of purified Drosophila germline stem cells. Developmental Biology. 283(2). 486–502. 102 indexed citations

16.

Ohlstein, Benjamin, et al.. (2004). The stem cell niche: theme and variations. Current Opinion in Cell Biology. 16(6). 693–699. 250 indexed citations

17.

Kai, Toshie & Allan C. Spradling. (2003). An empty Drosophila stem cell niche reactivates the proliferation of ectopic cells. Proceedings of the National Academy of Sciences. 100(8). 4633–4638. 220 indexed citations

18.

Spradling, Allan C., Daniela Drummond‐Barbosa, & Toshie Kai. (2001). Stem cells find their niche. Nature. 414(6859). 98–104. 1075 indexed citations breakdown →

19.

Kai, Toshie, et al.. (1999). Gene 61.3 of Bacteriophage T4 Is the spackle Gene. Virology. 260(2). 254–259. 11 indexed citations

20.

Kai, Toshie, Hiroyuki Ueno, & Tetsuro Yonesaki. (1998). Involvement of Other Bacteriophage T4 Genes in the Blockade of Protein Synthesis and mRNA Destabilization by a Mutation of Gene61.5. Virology. 248(1). 148–155. 17 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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