Yuka Maeda

12.4k total citations · 1 hit paper
32 papers, 1.6k citations indexed

About

Yuka Maeda is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Yuka Maeda has authored 32 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 11 papers in Immunology and 8 papers in Oncology. Recurrent topics in Yuka Maeda's work include Immunotherapy and Immune Responses (7 papers), Immune Cell Function and Interaction (7 papers) and T-cell and B-cell Immunology (6 papers). Yuka Maeda is often cited by papers focused on Immunotherapy and Immune Responses (7 papers), Immune Cell Function and Interaction (7 papers) and T-cell and B-cell Immunology (6 papers). Yuka Maeda collaborates with scholars based in Japan, United States and Switzerland. Yuka Maeda's co-authors include Hiroyoshi Nishikawa, Daisuke Sugiyama, Shimon Sakaguchi, Ichiro Katayama, Megumi Nishioka, Eiichi Sato, Yasuo Fukumori, Sachiko Ezoe, Julia Karbach and Elke Jäger and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Yuka Maeda

32 papers receiving 1.5k citations

Hit Papers

Anti-CCR4 mAb selectively depletes effector-type FoxP3+CD... 2013 2026 2017 2021 2013 100 200 300 400 500

Peers

Yuka Maeda
Karim Y. Helmy United States
Andrew Sprague United States
Rodrigo Jácamo United States
Erich C. Strauss United States
Karim Y. Helmy United States
Yuka Maeda
Citations per year, relative to Yuka Maeda Yuka Maeda (= 1×) peers Karim Y. Helmy

Countries citing papers authored by Yuka Maeda

Since Specialization
Citations

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

Fields of papers citing papers by Yuka Maeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuka Maeda

This figure shows the co-authorship network connecting the top 25 collaborators of Yuka Maeda. A scholar is included among the top collaborators of Yuka Maeda 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 Yuka Maeda. Yuka Maeda 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.
Maeda, Yuka, Hisashi Wada, Daisuke Sugiyama, et al.. (2021). Depletion of central memory CD8+ T cells might impede the antitumor therapeutic effect of Mogamulizumab. Nature Communications. 12(1). 7280–7280. 29 indexed citations
2.
Ebihara, Tadashi, et al.. (2021). Rolling-Shutter Sensor-Based Visible Light Communication with Cross-Screen Filter: Communication and Positioning System Using a Commercial Camera. 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE). 386–390. 5 indexed citations
3.
Maeda, Yuka, et al.. (2020). CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data. Bioinformatics. 37(11). 1632–1634. 4 indexed citations
4.
Maeda, Yuka, et al.. (2020). Model-based clustering for flow and mass cytometry data with clinical information. BMC Bioinformatics. 21(S13). 393–393. 3 indexed citations
5.
Sakurai, Toshihiro, et al.. (2020). Utility of the periareolar incision technique for breast reconstructive surgery in patients with breast cancer. Surgery Today. 50(9). 1008–1015. 10 indexed citations
6.
Sakurai, Takashi, et al.. (2019). Efficacy and Safety of Palbociclib and Fulvestrant in Japanese Patients With ER+/HER2− Advanced/Metastatic Breast Cancer. In Vivo. 33(6). 2037–2044. 6 indexed citations
7.
Sugiyama, Daisuke, Yuka Maeda, Allison Betof Warner, et al.. (2019). Selective inhibition of low-affinity memory CD8+ T cells by corticosteroids. The Journal of Experimental Medicine. 216(12). 2701–2713. 87 indexed citations
8.
Maeda, Yuka, et al.. (2019). Model-based cell clustering and population tracking for time-series flow cytometry data. BMC Bioinformatics. 20(S23). 633–633. 7 indexed citations
9.
Maeda, Yuka, Fumihiko Katagiri, Kentaro Hozumi, et al.. (2017). An Anti-Human Lutheran Glycoprotein Phage Antibody Inhibits Cell Migration on Laminin-511: Epitope Mapping of the Antibody. PLoS ONE. 12(1). e0167860–e0167860. 5 indexed citations
10.
Nagano, Kazuya, Yuka Maeda, Takuya Yamashita, et al.. (2014). Ephrin receptor A10 is a promising drug target potentially useful for breast cancers including triple negative breast cancers. Journal of Controlled Release. 189. 72–79. 41 indexed citations
11.
Nagano, Kazuya, Yuka Maeda, Takuya Yamashita, et al.. (2013). Abstract C119: Anti-EphA10 monoclonal antibody is a potential therapy against EphA10 positive breast cancer.. Molecular Cancer Therapeutics. 12(11_Supplement). C119–C119. 1 indexed citations
12.
Nagano, Kazuya, Takuya Yamashita, Yuka Maeda, et al.. (2013). Expression of Eph receptor A10 is correlated with lymph node metastasis and stage progression in breast cancer patients. Cancer Medicine. 2(6). 972–977. 32 indexed citations
13.
Noguchi, Takuro, Takuma Kato, Linan Wang, et al.. (2012). Intracellular Tumor-Associated Antigens Represent Effective Targets for Passive Immunotherapy. Cancer Research. 72(7). 1672–1682. 43 indexed citations
14.
Yamashita, Takuya, Kazuya Nagano, Yuka Maeda, et al.. (2012). Annexin A4 is a possible biomarker for cisplatin susceptibility of malignant mesothelioma cells. Biochemical and Biophysical Research Communications. 421(1). 140–144. 23 indexed citations
15.
Baba, Kenji, Hiroyuki Tanaka, Masaki Nishimura, et al.. (2011). Age-Dependent Deterioration of Peak Inspiratory Flow with Two Kinds of Dry Powder Corticosteroid Inhalers (Diskus R and Turbuhaler R ) and Relationships with Asthma Control. Journal of Aerosol Medicine and Pulmonary Drug Delivery. 24(6). 293–301. 10 indexed citations
16.
Muraoka, Daisuke, Takuma Kato, Linan Wang, et al.. (2010). Peptide Vaccine Induces Enhanced Tumor Growth Associated with Apoptosis Induction in CD8+ T Cells. The Journal of Immunology. 185(6). 3768–3776. 41 indexed citations
17.
Natsume, Atsushi, Motokazu Ito, Yutaka Kondo, et al.. (2008). Synergistic induction of NY-ESO-1 antigen expression by a novel histone deacetylase inhibitor, valproic acid, with 5-aza-2′-deoxycytidine in glioma cells. Journal of Neuro-Oncology. 92(1). 15–22. 42 indexed citations
18.
Yamamoto, Noritaka, Yuka Maeda, Aya Ikeda, & Hiroshi Sakurai. (2008). Regulation of Thermotolerance by Stress-Induced Transcription Factors in Saccharomyces cerevisiae. Eukaryotic Cell. 7(5). 783–790. 45 indexed citations
19.
Takeuchi, Hiroki, Atsushi Natsume, Toshihiko Wakabayashi, et al.. (2007). Intravenously transplanted human neural stem cells migrate to the injured spinal cord in adult mice in an SDF-1- and HGF-dependent manner. Neuroscience Letters. 426(2). 69–74. 95 indexed citations
20.
Maeda, Yuka, et al.. (1976). [Antitumour polysaccharides and host defence against cancer: A new way for cancer immuno-chemotherapy (author's transl)].. PubMed. 21(6). 425–35. 3 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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