Chikatoshi Kai

10.9k total citations
29 papers, 1.9k citations indexed

About

Chikatoshi Kai is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Chikatoshi Kai has authored 29 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 9 papers in Cancer Research and 3 papers in Genetics. Recurrent topics in Chikatoshi Kai's work include RNA and protein synthesis mechanisms (11 papers), RNA modifications and cancer (8 papers) and Cancer-related molecular mechanisms research (8 papers). Chikatoshi Kai is often cited by papers focused on RNA and protein synthesis mechanisms (11 papers), RNA modifications and cancer (8 papers) and Cancer-related molecular mechanisms research (8 papers). Chikatoshi Kai collaborates with scholars based in Japan, Australia and Italy. Chikatoshi Kai's co-authors include Piero Carninci, Jun Kawai, Yoshihide Hayashizaki, Martin C. Frith, Shiro Fukuda, Yoshihide Hayashizaki, Albin Sandelin, Mari Nakamura, Harukazu Suzuki and Hideya Kawaji and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and Nature Methods.

In The Last Decade

Chikatoshi Kai

29 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chikatoshi Kai Japan 22 1.7k 539 214 196 122 29 1.9k
Liana F. Lareau United States 12 3.0k 1.8× 422 0.8× 178 0.8× 198 1.0× 147 1.2× 15 3.2k
Søren Lykke‐Andersen Denmark 21 3.2k 1.9× 542 1.0× 183 0.9× 187 1.0× 136 1.1× 31 3.4k
Vedran Franke Germany 20 1.5k 0.9× 259 0.5× 247 1.2× 194 1.0× 153 1.3× 32 1.7k
Diego Borges-Rivera United States 7 1.5k 0.9× 323 0.6× 272 1.3× 174 0.9× 73 0.6× 10 1.8k
Galit Lev-Maor Israel 18 2.5k 1.5× 383 0.7× 442 2.1× 261 1.3× 112 0.9× 21 2.9k
Haidi Zhang China 15 1.8k 1.1× 908 1.7× 245 1.1× 159 0.8× 243 2.0× 34 2.3k
Nicolas Cougot France 11 2.1k 1.2× 797 1.5× 109 0.5× 89 0.5× 91 0.7× 11 2.3k
Manuel J. Muñoz Argentina 20 2.2k 1.3× 288 0.5× 165 0.8× 123 0.6× 89 0.7× 33 2.4k
Kevin Czaplinski United States 22 2.9k 1.7× 876 1.6× 166 0.8× 127 0.6× 92 0.8× 31 3.1k
Ann S. Zweig United States 12 1.8k 1.1× 399 0.7× 247 1.2× 428 2.2× 148 1.2× 16 2.3k

Countries citing papers authored by Chikatoshi Kai

Since Specialization
Citations

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

Fields of papers citing papers by Chikatoshi Kai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chikatoshi Kai

This figure shows the co-authorship network connecting the top 25 collaborators of Chikatoshi Kai. A scholar is included among the top collaborators of Chikatoshi 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 Chikatoshi Kai. Chikatoshi Kai 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.
Schaefer, Ulf, Rimantas Kodzius, Chikatoshi Kai, et al.. (2010). High Sensitivity TSS Prediction: Estimates of Locations Where TSS Cannot Occur. PLoS ONE. 5(11). e13934–e13934. 8 indexed citations
2.
Kawaura, Kanako, Keiichi Mochida, Akiko Enju, et al.. (2009). Assessment of adaptive evolution between wheat and rice as deduced from full-length common wheat cDNA sequence data and expression patterns. BMC Genomics. 10(1). 271–271. 34 indexed citations
3.
Fink, J. Lynn, Amit Mittal, Donald M. Gardiner, et al.. (2008). Towards defining the nuclear proteome. Genome biology. 9(1). R15–R15. 30 indexed citations
4.
Kawaji, Hideya, Mari Nakamura, Yukari Takahashi, et al.. (2008). Hidden layers of human small RNAs. BMC Genomics. 9(1). 157–157. 238 indexed citations
5.
Frith, Martin C., Piero Carninci, Chikatoshi Kai, et al.. (2007). Splicing bypasses 3′ end formation signals to allow complex gene architectures. Gene. 403(1-2). 188–193. 6 indexed citations
6.
Frith, Martin C., Jasmina Ponjavic, David Fredman, et al.. (2006). Evolutionary turnover of mammalian transcription start sites. Genome Research. 16(6). 713–722. 64 indexed citations
7.
Kodzius, Rimantas, Miki Kojima, Hiromi Nishiyori, et al.. (2006). CAGE: cap analysis of gene expression. Nature Methods. 3(3). 211–222. 288 indexed citations
8.
Wells, Christine A., Alistair M. Chalk, Alistair R. R. Forrest, et al.. (2006). Alternate transcription of the Toll-like receptor signaling cascade. Genome biology. 7(2). R10–R10. 68 indexed citations
9.
Bajić, Vladimir B., Sin Lam Tan, Alan Christoffels, et al.. (2006). Mice and Men: Their Promoter Properties. PLoS Genetics. 2(4). e54–e54. 82 indexed citations
10.
Forrest, Alistair R. R., Mark L. Crowe, Alistair M. Chalk, et al.. (2006). Genome-wide review of transcriptional complexity in mouse protein kinases and phosphatases. Genome biology. 7(1). R5–R5. 32 indexed citations
11.
Nimwegen, Erik van, Chikatoshi Kai, Jun Kawai, et al.. (2006). A Simple Physical Model Predicts Small Exon Length Variations. PLoS Genetics. 2(4). e45–e45. 62 indexed citations
12.
Furuno, Masaaki, Ken C. Pang, Noriko Ninomiya, et al.. (2006). Clusters of Internally Primed Transcripts Reveal Novel Long Noncoding RNAs. PLoS Genetics. 2(4). e37–e37. 137 indexed citations
13.
Frith, Martin C., Laurens Wilming, Alistair R. R. Forrest, et al.. (2006). Pseudo–Messenger RNA: Phantoms of the Transcriptome. PLoS Genetics. 2(4). e23–e23. 51 indexed citations
14.
Forrest, Alistair R. R., Darrin F. Taylor, J. Lynn Fink, et al.. (2006). PhosphoregDB: The tissue and sub-cellular distribution of mammalian protein kinases and phosphatases. BMC Bioinformatics. 7(1). 82–82. 15 indexed citations
15.
Frith, Martin C., Alistair R. R. Forrest, Ehsan Nourbakhsh, et al.. (2006). The Abundance of Short Proteins in the Mammalian Proteome. PLoS Genetics. 2(4). e52–e52. 183 indexed citations
16.
Ponjavic, Jasmina, Boris Lenhard, Chikatoshi Kai, et al.. (2006). Transcriptional and structural impact of TATA-initiation site spacing in mammalian core promoters. Genome biology. 7(8). R78–R78. 89 indexed citations
17.
Davis, Melissa J., Kelly Hanson, Francis Clark, et al.. (2006). Differential Use of Signal Peptides and Membrane Domains Is a Common Occurrence in the Protein Output of Transcriptional Units. PLoS Genetics. 2(4). e46–e46. 33 indexed citations
18.
Suzuki, Harukazu, Rintaro Saito, Mutsumi Kanamori, et al.. (2003). The Mammalian Protein–Protein Interaction Database and Its Viewing System That Is Linked to the Main FANTOM2 Viewer. Genome Research. 13(6b). 1534–1541. 18 indexed citations
19.
Kanamori, Mutsumi, Péter Sandy, Stefania Marzinotto, et al.. (2003). The PDZ Protein Tax-interacting Protein-1 Inhibits β-Catenin Transcriptional Activity and Growth of Colorectal Cancer Cells. Journal of Biological Chemistry. 278(40). 38758–38764. 73 indexed citations
20.
Kanamori, Mutsumi, Chikatoshi Kai, Yoshihide Hayashizaki, & Harukazu Suzuki. (2002). NF‐κB activator Act1 associates with IL‐1/Toll pathway adaptor molecule TRAF6. FEBS Letters. 532(1-2). 241–246. 37 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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