Kae Hashimoto

2.8k citations
74 papers · 2.3k · h-index 28

Impact in

Papers in

Kae Hashimoto

72 papers receiving 2.3k citations

Peers

Kae Hashimoto
Comparison fields: 5 of 100
  • Cancer Research 718
  • Reproductive Medicine 204
  • Immunology 508
  • Oncology 608
  • Molecular Biology 1.2k
Replace Bin Shi with:
Bin Shi China
Evgeny Yakirevich United States
Cecilia S. Leung United States
Stefania Canè Italy
Jan Dominik Kuhlmann Germany
Daichi Maeda Japan
Yasushi Saga Japan
Mitsuko Furuya Japan
Vanesa García‐Barberán Spain
Zhaohui Lu China
Kae Hashimoto relative to Bin Shi China Bin Shi's profile →
Citations per field
00.5×2×3×4.5×
Bin Shi · 1×
Citations per year

Countries citing papers authored by Kae Hashimoto

Since Specialization
Citations

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

Fields of papers citing papers by Kae Hashimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kae Hashimoto, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kae Hashimoto Line = papers co-authored together Kae Hashimoto links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016195
2 2015134
3 2018120
4 201094
5 202094
6 202094
7 201791
8 201384
9 201881
10 200778
11 202177
12 201568
13 201563
14 201858
15 201058
16 200756
17 200753
18 200545
19 201845
20 200941

About Kae Hashimoto

Kae Hashimoto is a scholar working on Cancer Research, Reproductive Medicine, Oncology, Immunology and Obstetrics and Gynecology, having authored 74 papers that have together received 2.3k indexed citations. Recurring topics across this work include Ovarian cancer diagnosis and treatment (9 papers), Immune cells in cancer (9 papers), Cancer Cells and Metastasis (7 papers), Circular RNAs in diseases (7 papers), MicroRNA in disease regulation (7 papers), Extracellular vesicles in disease (6 papers), Cancer, Hypoxia, and Metabolism (6 papers) and Endometrial and Cervical Cancer Treatments (5 papers). The work is most often cited by research in Cancer Research (718 citations), Reproductive Medicine (204 citations), Immunology (508 citations), Oncology (608 citations) and Molecular Biology (1.2k citations). Kae Hashimoto has collaborated with scholars based in Japan, United States and Canada. Frequent co-authors include Kenjiro Sawada, Seiji Mabuchi, Tadashi Kimura, Michiko Kodama, Yasuto Kinose, Koji Nakamura, Tadashi Kimura, Mahiru Kawano, Yuri Matsumoto and Erika Nakatsuka. Their work appears in journals such as Molecular Cancer Therapeutics, Biochemical and Biophysical Research Communications, Oncotarget, International Journal of Gynecological Cancer and Molecular Cancer Research.

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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