Kosuke Hashimoto

4.3k total citations
59 papers, 1.8k citations indexed

About

Kosuke Hashimoto is a scholar working on Molecular Biology, Oncology and Epidemiology. According to data from OpenAlex, Kosuke Hashimoto has authored 59 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 6 papers in Oncology and 5 papers in Epidemiology. Recurrent topics in Kosuke Hashimoto's work include Glycosylation and Glycoproteins Research (12 papers), Genomics and Phylogenetic Studies (7 papers) and Machine Learning in Bioinformatics (5 papers). Kosuke Hashimoto is often cited by papers focused on Glycosylation and Glycoproteins Research (12 papers), Genomics and Phylogenetic Studies (7 papers) and Machine Learning in Bioinformatics (5 papers). Kosuke Hashimoto collaborates with scholars based in Japan, United States and Italy. Kosuke Hashimoto's co-authors include Anna R. Panchenko, Hafumi Nishi, Minoru Kanehisa, Susumu Goto, Shin Kawano, Akiyasu C. Yoshizawa, Kiyoko F. Aoki‐Kinoshita, Shujiro Okuda, Stephen H. Bryant and Nobuhisa Ueda and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and SHILAP Revista de lepidopterología.

In The Last Decade

Kosuke Hashimoto

54 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kosuke Hashimoto Japan 20 1.3k 225 158 135 122 59 1.8k
Edda Kloppmann United States 14 1.2k 1.0× 225 1.0× 55 0.3× 91 0.7× 95 0.8× 17 1.7k
Natasha Thorne United States 18 1.2k 1.0× 141 0.6× 136 0.9× 71 0.5× 61 0.5× 29 2.4k
Horst Flotow Singapore 17 1.1k 0.9× 347 1.5× 126 0.8× 68 0.5× 128 1.0× 31 1.8k
Kevin Drew United States 19 1.9k 1.5× 101 0.4× 94 0.6× 97 0.7× 107 0.9× 29 2.3k
Sang Hoon Ha South Korea 24 1.8k 1.4× 171 0.8× 165 1.0× 67 0.5× 120 1.0× 49 2.6k
Seung‐Wook Chi South Korea 26 1.3k 1.0× 133 0.6× 114 0.7× 107 0.8× 89 0.7× 79 1.8k
Betty Shen United States 25 1.8k 1.5× 118 0.5× 111 0.7× 223 1.7× 141 1.2× 45 3.1k
Konstantin Denessiouk Finland 24 1.2k 0.9× 159 0.7× 58 0.4× 261 1.9× 190 1.6× 57 1.6k
Thomas Horn Germany 32 2.0k 1.6× 219 1.0× 188 1.2× 42 0.3× 151 1.2× 92 2.9k
Nurcan Tunçbağ Türkiye 22 1.9k 1.5× 123 0.5× 78 0.5× 310 2.3× 51 0.4× 50 2.4k

Countries citing papers authored by Kosuke Hashimoto

Since Specialization
Citations

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

Fields of papers citing papers by Kosuke Hashimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kosuke Hashimoto

This figure shows the co-authorship network connecting the top 25 collaborators of Kosuke Hashimoto. A scholar is included among the top collaborators of Kosuke Hashimoto 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 Kosuke Hashimoto. Kosuke Hashimoto 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.
Uchida, Takahiro, Kazuyuki Nakagome, Kosuke Hashimoto, et al.. (2025). Eosinophils as predictive biomarkers in anti-programmed cell death 1 monotherapy for non-small cell lung cancer. Frontiers in Immunology. 16. 1574314–1574314. 1 indexed citations
2.
Kaira, Kyoichi, Hisao Imai, Ayako Shiono, et al.. (2025). Prognostic factors of liver metastases in extensive-stage small cell lung cancer receiving chemo-immunotherapy. Translational Lung Cancer Research. 14(5). 1569–1581. 1 indexed citations
3.
Miwa, Toru, et al.. (2024). Optimizing Tinnitus Management: The Important Role of Hearing Aids with Sound Generators. Audiology Research. 14(4). 674–683. 1 indexed citations
4.
5.
Hashimoto, Kosuke, Toru Miwa, Kiyomitsu Nara, et al.. (2024). Gap Junction Beta-2 p.Val84Met Can Cause Autosomal Dominant Syndromic Hearing Loss With Keratoderma. Cureus. 16(2). e54992–e54992.
6.
Endo, Satoshi, Hisao Imai, Ayako Shiono, et al.. (2024). The Glasgow Prognostic Score as a Predictor of Survival after Chemoradiotherapy for Limited-Disease Small Cell Lung Cancer. Oncology. 103(2). 1–11.
7.
Hashimoto, Kosuke, et al.. (2023). Attention network for predicting T-cell receptor–peptide binding can associate attention with interpretable protein structural properties. SHILAP Revista de lepidopterología. 3. 1274599–1274599. 8 indexed citations
8.
Seki, Masafumi, et al.. (2022). Sequential Treatment by Antiviral Drugs Followed by Immunosuppressive Agents for COVID-19 Patients with Hematological Malignancy. Infection and Drug Resistance. Volume 15. 7117–7124. 4 indexed citations
9.
Hashimoto, Kosuke, Eeva‐Mari Jouhilahti, Virpi Töhönen, et al.. (2021). Embryonic LTR retrotransposons supply promoter modules to somatic tissues. Genome Research. 31(11). 1983–1993. 6 indexed citations
10.
Hon, Chung-Chau, Aimi Naim Abdullah, João Wosniak, et al.. (2020). Analysis of splice variants of the human protein disulfide isomerase (P4HB) gene. BMC Genomics. 21(1). 766–766. 5 indexed citations
11.
Taguchi, Ayumi, Kazunori Nagasaka, Charles Plessy, et al.. (2020). Use of Cap Analysis Gene Expression to detect human papillomavirus promoter activity patterns at different disease stages. Scientific Reports. 10(1). 17991–17991. 4 indexed citations
12.
Yamanaka, Soichiro, Hidenori Nishihara, Hidehiro Toh, et al.. (2019). Broad Heterochromatic Domains Open in Gonocyte Development Prior to De Novo DNA Methylation. Developmental Cell. 51(1). 21–34.e5. 27 indexed citations
13.
Pawlak, Michał, Jordan A. Ramilowski, Kosuke Hashimoto, et al.. (2019). Dynamics of cardiomyocyte transcriptome and chromatin landscape demarcates key events of heart development. Genome Research. 29(3). 506–519. 20 indexed citations
14.
Hashimoto, Kosuke, Ana Maria Suzuki, Alexandre Dos Santos, et al.. (2015). CAGE profiling of ncRNAs in hepatocellular carcinoma reveals widespread activation of retroviral LTR promoters in virus-induced tumors. Genome Research. 25(12). 1812–1824. 43 indexed citations
15.
Levillayer, Florence, Grégory Jouvion, Laurence Fiette, et al.. (2015). Deficiency of multidrug resistance 2 contributes to cell transformation through oxidative stress. Carcinogenesis. 37(1). 39–48. 13 indexed citations
16.
Hashimoto, Kosuke, et al.. (2013). PIV Imaging Technique with Consumer Digital Still Cameras. 38(1). 11–14. 1 indexed citations
17.
Mouri, Hideaki, et al.. (2012). Large-scale length that determines the mean rate of energy dissipation in turbulence. Physical Review E. 86(2). 26309–26309. 8 indexed citations
18.
Hashimoto, Kosuke, Thomas Madej, Stephen H. Bryant, & Anna R. Panchenko. (2010). Functional States of Homooligomers: Insights from the Evolution of Glycosyltransferases. Journal of Molecular Biology. 399(1). 196–206. 37 indexed citations
19.
Hashimoto, Kosuke, Akiyasu C. Yoshizawa, Shujiro Okuda, et al.. (2007). The repertoire of desaturases and elongases reveals fatty acid variations in 56 eukaryotic genomes. Journal of Lipid Research. 49(1). 183–191. 160 indexed citations
20.
Hashimoto, Kosuke, et al.. (2003). GLYCAN: The Database of Carbohydrate Structures. Proceedings Genome Informatics Workshop/Genome informatics. 14(14). 649–650. 4 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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