Mika Kimura

2.6k total citations · 1 hit paper
40 papers, 2.0k citations indexed

About

Mika Kimura is a scholar working on Molecular Biology, Genetics and Surgery. According to data from OpenAlex, Mika Kimura has authored 40 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 15 papers in Genetics and 5 papers in Surgery. Recurrent topics in Mika Kimura's work include Epigenetics and DNA Methylation (18 papers), CRISPR and Genetic Engineering (9 papers) and Genetic Syndromes and Imprinting (8 papers). Mika Kimura is often cited by papers focused on Epigenetics and DNA Methylation (18 papers), CRISPR and Genetic Engineering (9 papers) and Genetic Syndromes and Imprinting (8 papers). Mika Kimura collaborates with scholars based in Japan, Russia and United States. Mika Kimura's co-authors include Takuro Horii, Izuho Hatada, Sumiyo Morita, Takahiro Ochiya, Kinichi Nakashima, Kazuhiko Nakabayashi, Kenichiro Hata, Hideyuki Nakashima, K. Okamura and Hirofumi Noguchi and has published in prestigious journals such as Cell, Nature Communications and Nature Biotechnology.

In The Last Decade

Mika Kimura

38 papers receiving 2.0k citations

Hit Papers

Targeted DNA demethylation in vivo using dCas9–peptide re... 2016 2026 2019 2022 2016 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mika Kimura Japan 23 1.7k 467 318 116 116 40 2.0k
Anthony D’Ippolito United States 10 2.3k 1.4× 484 1.0× 185 0.6× 162 1.4× 70 0.6× 20 2.6k
Baisong Lu United States 27 1.4k 0.8× 629 1.3× 201 0.6× 38 0.3× 108 0.9× 63 1.9k
Efrain Sanchez‐Ortiz United States 19 2.4k 1.4× 757 1.6× 120 0.4× 171 1.5× 152 1.3× 24 2.7k
Xu Jiang China 26 1.4k 0.8× 171 0.4× 561 1.8× 30 0.3× 71 0.6× 59 2.1k
Szymon Czauderna Poland 5 945 0.6× 230 0.5× 105 0.3× 44 0.4× 49 0.4× 7 1.1k
Takehito Kaneko Japan 24 1.4k 0.8× 864 1.9× 58 0.2× 64 0.6× 66 0.6× 80 2.2k
Benedikt Wefers Germany 17 1.4k 0.8× 429 0.9× 39 0.1× 115 1.0× 181 1.6× 31 1.9k
Bruno Di Stefano United States 22 2.0k 1.2× 463 1.0× 154 0.5× 11 0.1× 113 1.0× 40 2.3k
Anne Harrington United States 13 1.1k 0.6× 289 0.6× 112 0.4× 15 0.1× 120 1.0× 20 1.5k
Birger Voigt Japan 16 953 0.6× 482 1.0× 39 0.1× 37 0.3× 123 1.1× 35 1.3k

Countries citing papers authored by Mika Kimura

Since Specialization
Citations

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

Fields of papers citing papers by Mika Kimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mika Kimura

This figure shows the co-authorship network connecting the top 25 collaborators of Mika Kimura. A scholar is included among the top collaborators of Mika Kimura 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 Mika Kimura. Mika Kimura 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.
Morita, Sumiyo, et al.. (2025). Germline epigenome editing identifies H3K9me3 as a mediator of intergenerational DNA methylation recovery in mice. Nature Communications. 16(1). 11200–11200.
2.
Morita, Sumiyo, Takuro Horii, Mika Kimura, et al.. (2023). A Lipid Nanoparticle-Based Method for the Generation of Liver-Specific Knockout Mice. International Journal of Molecular Sciences. 24(18). 14299–14299. 1 indexed citations
3.
Horii, Takuro, Sumiyo Morita, Mika Kimura, & Izuho Hatada. (2022). Efficient generation of epigenetic disease model mice by epigenome editing using the piggyBac transposon system. Epigenetics & Chromatin. 15(1). 40–40. 4 indexed citations
4.
Horii, Takuro, Sumiyo Morita, Shinjiro Hino, et al.. (2020). Successful generation of epigenetic disease model mice by targeted demethylation of the epigenome. Genome biology. 21(1). 77–77. 45 indexed citations
5.
Horii, Takuro, et al.. (2017). Efficient generation of conditional knockout mice via sequential introduction of lox sites. Scientific Reports. 7(1). 7891–7891. 43 indexed citations
6.
Morita, Sumiyo, Kazuhiko Nakabayashi, Tomoko Kawai, et al.. (2016). Gene expression profiling of white adipose tissue reveals paternal transmission of proneness to obesity. Scientific Reports. 6(1). 21693–21693. 8 indexed citations
7.
Hirano, Hisato, Jonathan S. Gootenberg, Takuro Horii, et al.. (2016). Structure and Engineering of Francisella novicida Cas9. Cell. 164(5). 950–961. 273 indexed citations
8.
Horii, Takuro, Masamichi Yamamoto, Sumiyo Morita, et al.. (2015). p53 Suppresses Tetraploid Development in Mice. Scientific Reports. 5(1). 8907–8907. 28 indexed citations
9.
Morita, Sumiyo, Takuro Horii, Mika Kimura, et al.. (2014). Paternal Allele Influences High Fat Diet-Induced Obesity. PLoS ONE. 9(1). e85477–e85477. 16 indexed citations
10.
Horii, Takuro, et al.. (2010). Epigenetic Differences between Embryonic Stem Cells Generated from Blastocysts Developed In Vitro and In Vivo. Cellular Reprogramming. 12(5). 551–563. 12 indexed citations
11.
Morita, Sumiyo, Akemi Hara, Itaru Kojima, et al.. (2009). Dicer Is Required for Maintaining Adult Pancreas. PLoS ONE. 4(1). e4212–e4212. 38 indexed citations
12.
Hatada, Izuho, Masakazu Namihira, Sumiyo Morita, et al.. (2008). Astrocyte-Specific Genes Are Generally Demethylated in Neural Precursor Cells Prior to Astrocytic Differentiation. PLoS ONE. 3(9). e3189–e3189. 59 indexed citations
13.
Hatada, Izuho, Sumiyo Morita, Mika Kimura, et al.. (2008). Genome-wide demethylation during neural differentiation of P19 embryonal carcinoma cells. Journal of Human Genetics. 53(2). 185–191. 19 indexed citations
14.
Kimura, Mika, Takuro Horii, Sumiyo Morita, et al.. (2008). MeCP2-dependent repression of an imprinted miR-184 released by depolarization. Human Molecular Genetics. 17(8). 1192–1199. 100 indexed citations
15.
Morita, Sumiyo, Takuro Horii, Mika Kimura, et al.. (2007). One Argonaute family member, Eif2c2 (Ago2), is essential for development and appears not to be involved in DNA methylation. Genomics. 89(6). 687–696. 119 indexed citations
16.
Kimura, Mika, Sumiyo Morita, Kenichi Matsubara, et al.. (2006). Microarray analysis of promoter methylation in lung cancers. Journal of Human Genetics. 51(4). 368–374. 79 indexed citations
17.
Hashizume, Naotaka, et al.. (2004). Response to Blood Glucose and Insulin by Japanese Foods in Healthy Subjects. 25(3). 222–225. 11 indexed citations
18.
Ishizuka, Tatsuo, Atsushi Miura, Kimihiro Kajita, et al.. (2002). Effect of 1α,25-dihydroxy vitamin D3 and vitamin E on insulin-induced glucose uptake in rat adipocytes. Diabetes Research and Clinical Practice. 55(3). 175–183. 22 indexed citations
19.
Miura, Atsushi, Kimihiro Kajita, Masayoshi Ishizawa, et al.. (2001). Differential Effect of PKC Isoform on Insulin‐ and Phorbol Ester‐Stimulated Glucose Uptake Mechanism in Rat Adipocytes. IUBMB Life. 51(5). 299–304. 4 indexed citations
20.

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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