Dai Fujikawa

734 total citations
11 papers, 437 citations indexed

About

Dai Fujikawa is a scholar working on Immunology, Ecology, Evolution, Behavior and Systematics and Molecular Biology. According to data from OpenAlex, Dai Fujikawa has authored 11 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Immunology, 6 papers in Ecology, Evolution, Behavior and Systematics and 5 papers in Molecular Biology. Recurrent topics in Dai Fujikawa's work include T-cell and Retrovirus Studies (9 papers), Vector-Borne Animal Diseases (6 papers) and Animal Disease Management and Epidemiology (4 papers). Dai Fujikawa is often cited by papers focused on T-cell and Retrovirus Studies (9 papers), Vector-Borne Animal Diseases (6 papers) and Animal Disease Management and Epidemiology (4 papers). Dai Fujikawa collaborates with scholars based in Japan, United States and Italy. Dai Fujikawa's co-authors include Toshiki Watanabe, Makoto Yamagishi, Kaoru Uchimaru, Makoto Nakashima, Atae Utsunomiya, Kazumi Nakano, Yuetsu Tanaka, Masako Iwanaga, Seiichiro Kobayashi and Makoto Hori and has published in prestigious journals such as Blood, Journal of Virology and Scientific Reports.

In The Last Decade

Dai Fujikawa

11 papers receiving 432 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dai Fujikawa Japan 8 241 188 137 130 53 11 437
G Feuer United States 9 209 0.9× 75 0.4× 128 0.9× 123 0.9× 42 0.8× 13 345
Amanda R. Panfil United States 12 235 1.0× 159 0.8× 111 0.8× 119 0.9× 118 2.2× 29 477
Andrea K. Thoma‐Kress Germany 13 323 1.3× 116 0.6× 197 1.4× 219 1.7× 47 0.9× 29 472
Christophe Debacq Belgium 12 383 1.6× 184 1.0× 167 1.2× 190 1.5× 49 0.9× 15 581
Laurent Dianoux France 12 307 1.3× 396 2.1× 94 0.7× 84 0.6× 69 1.3× 23 609
Takayuki Nitta United States 14 144 0.6× 198 1.1× 54 0.4× 65 0.5× 59 1.1× 25 433
Takaharu Ueno Japan 12 221 0.9× 128 0.7× 117 0.9× 123 0.9× 46 0.9× 27 458
Luca Casareto United States 9 431 1.8× 126 0.7× 258 1.9× 260 2.0× 118 2.2× 10 581
Shibani Mitra‐Kaushik United States 12 277 1.1× 189 1.0× 43 0.3× 45 0.3× 67 1.3× 17 446
N Lohrey United States 8 184 0.8× 219 1.2× 75 0.5× 103 0.8× 56 1.1× 10 486

Countries citing papers authored by Dai Fujikawa

Since Specialization
Citations

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

Fields of papers citing papers by Dai Fujikawa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dai Fujikawa

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

All Works

11 of 11 papers shown
1.
Yamagishi, Makoto, Makoto Hori, Dai Fujikawa, et al.. (2019). Targeting Excessive EZH1 and EZH2 Activities for Abnormal Histone Methylation and Transcription Network in Malignant Lymphomas. Cell Reports. 29(8). 2321–2337.e7. 128 indexed citations
2.
Galli, Veronica, Christopher C. Nixon, Nataša Štrbo, et al.. (2019). Essential Role of Human T Cell Leukemia Virus Type 1 orf-I in Lethal Proliferation of CD4 + Cells in Humanized Mice. Journal of Virology. 93(19). 15 indexed citations
3.
Yamagishi, Makoto, Dai Fujikawa, Toshiki Watanabe, & Kaoru Uchimaru. (2018). HTLV-1-Mediated Epigenetic Pathway to Adult T-Cell Leukemia–Lymphoma. Frontiers in Microbiology. 9. 1686–1686. 35 indexed citations
4.
Omsland, Maria, Cynthia A. Pise-Masison, Dai Fujikawa, et al.. (2018). Inhibition of Tunneling Nanotube (TNT) Formation and Human T-cell Leukemia Virus Type 1 (HTLV-1) Transmission by Cytarabine. Scientific Reports. 8(1). 11118–11118. 52 indexed citations
5.
Yamagishi, Makoto, Makoto Hori, Dai Fujikawa, et al.. (2016). Development and Molecular Analysis of Synthetic Lethality By Targeting EZH1 and EZH2 in Non-Hodgkin Lymphomas. Blood. 128(22). 462–462. 9 indexed citations
6.
Fujikawa, Dai, Shota Nakagawa, Makoto Hori, et al.. (2016). Polycomb-dependent epigenetic landscape in adult T-cell leukemia. Blood. 127(14). 1790–1802. 123 indexed citations
7.
Fujikawa, Dai, et al.. (2015). Epigenetic Heterogeneity in HIV-1 Latency Establishment. Scientific Reports. 5(1). 7701–7701. 55 indexed citations
9.
Yamagishi, Makoto, Kazumi Nakano, Toshiko Yamochi, et al.. (2014). Epigenetic deregulation of Ellis Van Creveld confers robust Hedgehog signaling in adult T‐cell leukemia. Cancer Science. 105(9). 1160–1169. 14 indexed citations
10.
Fujikawa, Dai, Makoto Yamagishi, Takaomi Ishida, et al.. (2014). HTLV-1 Tax disrupts the host epigenome by interacting with a Polycomb group protein EZH2. Retrovirology. 11(S1). 2 indexed citations
11.
Yamagishi, Makoto, Dai Fujikawa, Naoki Sakai, et al.. (2014). Molecular hallmarks of adult T cell leukemia: miRNA, epigenetics, and emerging signaling abnormalities. Retrovirology. 11(S1). 1 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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