Michi Miura

824 total citations
20 papers, 527 citations indexed

About

Michi Miura is a scholar working on Immunology, Agronomy and Crop Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Michi Miura has authored 20 papers receiving a total of 527 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Immunology, 11 papers in Agronomy and Crop Science and 10 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Michi Miura's work include T-cell and Retrovirus Studies (13 papers), Animal Disease Management and Epidemiology (11 papers) and Vector-Borne Animal Diseases (10 papers). Michi Miura is often cited by papers focused on T-cell and Retrovirus Studies (13 papers), Animal Disease Management and Epidemiology (11 papers) and Vector-Borne Animal Diseases (10 papers). Michi Miura collaborates with scholars based in Japan, United Kingdom and China. Michi Miura's co-authors include Charles R. M. Bangham, Masao Matsuoka, Anat Melamed, Paola Miyazato, Jun‐ichirou Yasunaga, Kenji Sugata, Hiroko Yaguchi, Kisato Nosaka, Michinori Kohara and Hirofumi Akari and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Blood and PLoS ONE.

In The Last Decade

Michi Miura

20 papers receiving 524 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michi Miura Japan 12 353 286 275 107 37 20 527
Andrea K. Thoma‐Kress Germany 13 323 0.9× 219 0.8× 197 0.7× 116 1.1× 26 0.7× 29 472
Abel Ureta‐Vidal France 12 409 1.2× 276 1.0× 242 0.9× 195 1.8× 51 1.4× 14 631
Gilles Gaudray France 7 577 1.6× 463 1.6× 437 1.6× 106 1.0× 35 0.9× 7 680
Sabine Thébault France 12 540 1.5× 420 1.5× 409 1.5× 206 1.9× 38 1.0× 15 735
Cecilia Tami United States 12 191 0.5× 270 0.9× 144 0.5× 222 2.1× 59 1.6× 15 675
S M Pettiford United States 9 256 0.7× 167 0.6× 174 0.6× 145 1.4× 23 0.6× 9 401
Noreen Sheehy Ireland 15 395 1.1× 307 1.1× 287 1.0× 174 1.6× 39 1.1× 33 676
Sébastien Alain Chevalier France 17 610 1.7× 493 1.7× 412 1.5× 179 1.7× 24 0.6× 27 813
Estelle Chiari United States 8 231 0.7× 151 0.5× 142 0.5× 167 1.6× 49 1.3× 10 393
Chloé Journo France 17 471 1.3× 336 1.2× 309 1.1× 168 1.6× 22 0.6× 34 641

Countries citing papers authored by Michi Miura

Since Specialization
Citations

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

Fields of papers citing papers by Michi Miura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michi Miura

This figure shows the co-authorship network connecting the top 25 collaborators of Michi Miura. A scholar is included among the top collaborators of Michi Miura 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 Michi Miura. Michi Miura 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.
Yasunaga, Jun‐ichirou, Michi Miura, Yusuke Higuchi, et al.. (2025). Identification of AK4 and RHOC as potential oncogenes addicted by adult T cell leukemia. Proceedings of the National Academy of Sciences. 122(8). e2416412122–e2416412122. 1 indexed citations
2.
Miura, Michi, Tadasuke Naito, & Mineki Saito. (2022). Current Perspectives in Human T-Cell Leukemia Virus Type 1 Infection and Its Associated Diseases. Frontiers in Medicine. 9. 867478–867478. 19 indexed citations
3.
Miura, Michi, et al.. (2022). Time-course of host cell transcription during the HTLV-1 transcriptional burst. PLoS Pathogens. 18(5). e1010387–e1010387. 10 indexed citations
4.
Miura, Michi & Honglin Chen. (2020). CUT&RUN detects distinct DNA footprints of RNA polymerase II near the transcription start sites. Chromosome Research. 28(3-4). 381–393. 11 indexed citations
5.
Miura, Michi, et al.. (2019). Kinetics of HTLV-1 reactivation from latency quantified by single-molecule RNA FISH and stochastic modelling. PLoS Pathogens. 15(11). e1008164–e1008164. 28 indexed citations
6.
Bangham, Charles R. M., et al.. (2019). Regulation of Latency in the Human T Cell Leukemia Virus, HTLV-1. Annual Review of Virology. 6(1). 365–385. 25 indexed citations
7.
Melamed, Anat, Hiroko Yaguchi, Michi Miura, et al.. (2018). The human leukemia virus HTLV-1 alters the structure and transcription of host chromatin in cis. eLife. 7. 57 indexed citations
8.
Furuta, Rie, Jun‐ichirou Yasunaga, Michi Miura, et al.. (2017). Human T-cell leukemia virus type 1 infects multiple lineage hematopoietic cells in vivo. PLoS Pathogens. 13(11). e1006722–e1006722. 68 indexed citations
9.
Satou, Yorifumi, Paola Miyazato, Ko Ishihara, et al.. (2016). The retrovirus HTLV-1 inserts an ectopic CTCF-binding site into the human genome. Proceedings of the National Academy of Sciences. 113(11). 3054–3059. 97 indexed citations
10.
Yasunaga, Jun‐ichirou, Rie Furuta, Michi Miura, et al.. (2016). Hematopoietic Stem Cell Infected with HTLV-1 Functions As a Viral Reservoir In Vivo. Blood. 128(22). 1343–1343. 6 indexed citations
11.
Sugata, Kenji, Jun‐ichirou Yasunaga, Michi Miura, et al.. (2016). Enhancement of anti-STLV-1/HTLV-1 immune responses through multimodal effects of anti-CCR4 antibody. Scientific Reports. 6(1). 18 indexed citations
12.
Sugata, Kenji, Jun‐ichirou Yasunaga, Yuichi Mitobe, et al.. (2015). Protective effect of cytotoxic T lymphocytes targeting HTLV-1 bZIP factor. Blood. 126(9). 1095–1105. 59 indexed citations
13.
Miura, Michi, Kenji Sugata, Tiejun Zhao, et al.. (2014). STLV-1-infected Japanese macaque as a model of HTLV-1 infection. Retrovirology. 11(S1). 2 indexed citations
14.
Miura, Michi, Jun‐ichirou Yasunaga, Kenji Sugata, et al.. (2013). Characterization of simian T-cell leukemia virus type 1 in naturally infected Japanese macaques as a model of HTLV-1 infection. Retrovirology. 10(1). 118–118. 38 indexed citations
15.
Shibata, Keitaro, et al.. (2012). A Single Protofilament Is Sufficient to Support Unidirectional Walking of Dynein and Kinesin. PLoS ONE. 7(8). e42990–e42990. 11 indexed citations
16.
Miura, Michi, Aiko Matsubara, Takuya Kobayashi, Masaki Edamatsu, & Yoko Y. Toyoshima. (2010). Nucleotide‐dependent behavior of single molecules of cytoplasmic dynein on microtubules in vitro. FEBS Letters. 584(11). 2351–2355. 17 indexed citations
17.
Sano, Y., Mikiko Aoki, H. Takahashi, et al.. (2005). The First Record of Dirofilaria immitis Infection in a Humboldt Penguin, Spheniscus humboldti. Journal of Parasitology. 91(5). 1235–1237. 16 indexed citations
18.
Yamashita, Tomoya, et al.. (2002). TARC in allergic disease. Allergy. 57(2). 180–181. 32 indexed citations
19.
Miura, Michi, et al.. (1997). Chronic infantile neurological cutaneous articular syndrome in a patient from Japan. European Journal of Pediatrics. 156(8). 624–626. 7 indexed citations
20.
Tsuji, Tsutomu, et al.. (1993). [A method for the quantification of interleukin-4 in serum (sandwich ELISA) and IL-4 levels in patients with atopic dermatitis].. PubMed. 42(7). 878–82. 5 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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