Kazuki Ichikawa

883 total citations
11 papers, 276 citations indexed

About

Kazuki Ichikawa is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Kazuki Ichikawa has authored 11 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 4 papers in Genetics and 2 papers in Cellular and Molecular Neuroscience. Recurrent topics in Kazuki Ichikawa's work include RNA modifications and cancer (4 papers), Epigenetics and DNA Methylation (4 papers) and Genomics and Phylogenetic Studies (3 papers). Kazuki Ichikawa is often cited by papers focused on RNA modifications and cancer (4 papers), Epigenetics and DNA Methylation (4 papers) and Genomics and Phylogenetic Studies (3 papers). Kazuki Ichikawa collaborates with scholars based in Japan, United States and Canada. Kazuki Ichikawa's co-authors include Shinichi Morishita, Jun Yoshimura, Hiroyuki Takeda, Ryohei Nakamura, Yuta Suzuki, Wei Qü, Andrew Fire, Idan Gabdank, Mark L. Edgley and Karen L. Artiles and has published in prestigious journals such as Nature Communications, Bioinformatics and Development.

In The Last Decade

Kazuki Ichikawa

11 papers receiving 273 citations

Peers

Kazuki Ichikawa

MB

GENET

PS

AGING

ECOLO

Mike Lyne United Kingdom

Dmitry Oshchepkov Russia

Liam Childs Germany

Yazmin L. Serrano Negron United States

Mark Woodbridge United Kingdom

Patrizia Tritto Italy

Scott Cain United States

Longhai Luo China

Norah Sadowski United States

Dmitry Prokopov Russia

Mike Lyne United Kingdom

Kazuki Ichikawa

216 ×1.0

MB

45 ×0.5

GENET

36 ×0.5

PS

10 ×0.3

AGING

23 ×1.1

ECOLO

Citations per year, relative to Kazuki Ichikawa Kazuki Ichikawa (= 1×) peers Mike Lyne

Countries citing papers authored by Kazuki Ichikawa

Since Specialization

Citations

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

Fields of papers citing papers by Kazuki Ichikawa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kazuki Ichikawa

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

All Works

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11 of 11 papers shown

1.

Ichikawa, Kazuki, Massa J. Shoura, Karen L. Artiles, et al.. (2025). CGC1, a new reference genome for Caenorhabditis elegans. Genome Research. 35(8). 1902–1918. 1 indexed citations

2.

Oka, Masahiro, Yoichi Miyamoto, Jun Adachi, et al.. (2023). Phase-separated nuclear bodies of nucleoporin fusions promote condensation of MLL1/CRM1 and rearrangement of 3D genome structure. Cell Reports. 42(8). 112884–112884. 22 indexed citations

3.

Ichikawa, Kazuki, et al.. (2023). A landscape of complex tandem repeats within individual human genomes. Nature Communications. 14(1). 5530–5530. 6 indexed citations

4.

Morishita, Shinichi, Kazuki Ichikawa, & Eugene W. Myers. (2020). Finding long tandem repeats in long noisy reads. Bioinformatics. 37(5). 612–621. 4 indexed citations

5.

Yoshimura, Jun, Kazuki Ichikawa, Massa J. Shoura, et al.. (2019). Recompleting the Caenorhabditis elegans genome. Genome Research. 29(6). 1009–1022. 75 indexed citations

6.

Ichikawa, Kazuki, Yuta Suzuki, Ryohei Nakamura, et al.. (2017). Centromere evolution and CpG methylation during vertebrate speciation. Nature Communications. 8(1). 1833–1833. 68 indexed citations

7.

Suzuki, Yuta, Jonas Korlach, Stephen W. Turner, et al.. (2016). AgIn: measuring the landscape of CpG methylation of individual repetitive elements. Bioinformatics. 32(19). 2911–2919. 20 indexed citations

8.

Ichikawa, Kazuki & Shinichi Morishita. (2015). A linear time algorithm for detecting long genomic regions enriched with a specific combination of epigenetic states. BMC Genomics. 16(S2). S8–S8. 3 indexed citations

9.

Ichikawa, Kazuki & Shinichi Morishita. (2014). A Simple but Powerful Heuristic Method for Accelerating <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula> -Means Clustering of Large-Scale Data in Life Science. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 11(4). 681–692. 9 indexed citations

10.

Nakamura, Ryohei, Tatsuya Tsukahara, Wei Qü, et al.. (2014). Large hypomethylated domains serve as strong repressive machinery for key developmental genes in vertebrates. Development. 141(13). 2568–2580. 33 indexed citations

11.

Qü, Wei, Shinichi Hashimoto, Atsuko Shimada, et al.. (2012). Genome-wide genetic variations are highly correlated with proximal DNA methylation patterns. Genome Research. 22(8). 1419–1425. 35 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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