Donna D. Zhang

28.6k total citations · 13 hit papers
119 papers, 16.6k citations indexed

About

Donna D. Zhang is a scholar working on Molecular Biology, Organic Chemistry and Epidemiology. According to data from OpenAlex, Donna D. Zhang has authored 119 papers receiving a total of 16.6k indexed citations (citations by other indexed papers that have themselves been cited), including 104 papers in Molecular Biology, 12 papers in Organic Chemistry and 10 papers in Epidemiology. Recurrent topics in Donna D. Zhang's work include Genomics, phytochemicals, and oxidative stress (81 papers), Glutathione Transferases and Polymorphisms (34 papers) and Arsenic contamination and mitigation (9 papers). Donna D. Zhang is often cited by papers focused on Genomics, phytochemicals, and oxidative stress (81 papers), Glutathione Transferases and Polymorphisms (34 papers) and Arsenic contamination and mitigation (9 papers). Donna D. Zhang collaborates with scholars based in United States, China and Czechia. Donna D. Zhang's co-authors include Matthew Dodson, Mark Hannink, Eli Chapman, Montserrat Rojo de la Vega, Melba C. Jaramillo, Raúl Castro-Portuguez, Zheng Sun, Tao Jiang, Cody J. Schmidlin and Deyu Fang and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Donna D. Zhang

117 papers receiving 16.5k citations

Hit Papers

NRF2 plays a critical rol... 2003 2026 2010 2018 2019 2003 2018 2013 2004 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Donna D. Zhang United States 58 12.7k 2.8k 2.6k 1.4k 1.2k 119 16.6k
Wen‐Chi Hou Taiwan 53 7.6k 0.6× 3.3k 1.2× 4.2k 1.6× 1.4k 1.1× 1.3k 1.1× 219 14.1k
Ann M. Bode United States 77 12.4k 1.0× 3.3k 1.2× 1.2k 0.5× 1.3k 0.9× 1.6k 1.4× 422 21.1k
Sanjay Gupta United States 69 7.2k 0.6× 2.6k 0.9× 1.7k 0.6× 537 0.4× 835 0.7× 286 15.1k
Jiřı́ Neužil Australia 63 7.5k 0.6× 2.9k 1.0× 888 0.3× 721 0.5× 664 0.6× 237 12.0k
Nobunao Wakabayashi United States 38 14.2k 1.1× 1.3k 0.4× 600 0.2× 1.2k 0.9× 1.3k 1.1× 61 17.7k
Chuanshu Huang United States 61 7.9k 0.6× 2.8k 1.0× 652 0.2× 839 0.6× 1.2k 1.0× 301 13.0k
Taeg Kyu Kwon South Korea 62 7.3k 0.6× 1.7k 0.6× 558 0.2× 1.2k 0.9× 1.8k 1.5× 398 13.4k
Anil K. Jaiswal United States 58 10.8k 0.9× 1.4k 0.5× 467 0.2× 997 0.7× 1.2k 1.0× 228 16.1k
Wun‐Jae Kim South Korea 53 5.9k 0.5× 2.1k 0.8× 2.0k 0.8× 557 0.4× 918 0.8× 418 10.7k
Masuko Ushio‐Fukai United States 60 7.3k 0.6× 1.3k 0.5× 886 0.3× 792 0.6× 3.5k 2.9× 125 17.2k

Countries citing papers authored by Donna D. Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Donna D. Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Donna D. Zhang. 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 Donna D. Zhang. The network helps show where Donna D. Zhang may publish in the future.

Co-authorship network of co-authors of Donna D. Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Donna D. Zhang. A scholar is included among the top collaborators of Donna D. Zhang 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 Donna D. Zhang. Donna D. Zhang 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.
Zhang, Donna D.. (2025). Thirty years of NRF2: advances and therapeutic challenges. Nature Reviews Drug Discovery. 24(6). 421–444. 47 indexed citations breakdown →
2.
Zhang, Donna D.. (2024). Natural inhibitor found for cell death by ferroptosis. Nature. 626(7998). 269–270. 6 indexed citations
3.
Zhang, Donna D.. (2024). Ironing out the details of ferroptosis. Nature Cell Biology. 26(9). 1386–1393. 59 indexed citations breakdown →
4.
Ambrose, Andrew J., Jared Sivinski, Xiaoyi Zhu, et al.. (2024). Human Hsp70 Substrate-Binding Domains Recognize Distinct Client Proteins. Biochemistry. 63(3). 251–263. 3 indexed citations
5.
Shakya, Aryatara, Pengfei Liu, Matthew Dodson, et al.. (2023). The NRF2-p97-NRF2 negative feedback loop. Redox Biology. 65. 102839–102839. 15 indexed citations
6.
Shakya, Aryatara, et al.. (2023). Anti-Ferroptotic Effects of Nrf2: Beyond the Antioxidant Response. Molecules and Cells. 46(3). 165–175. 61 indexed citations
7.
Ambrose, Andrew J., Jared Sivinski, Xiaoyi Zhu, et al.. (2022). Discovery and Development of a Selective Inhibitor of the ER Resident Chaperone Grp78. Journal of Medicinal Chemistry. 66(1). 677–694. 7 indexed citations
8.
Dodson, Matthew, Aryatara Shakya, Annadurai Anandhan, et al.. (2022). NRF2 and Diabetes: The Good, the Bad, and the Complex. Diabetes. 71(12). 2463–2476. 56 indexed citations
9.
Liu, Pengfei, Annadurai Anandhan, Jinjing Chen, et al.. (2022). Decreased autophagosome biogenesis, reduced NRF2, and enhanced ferroptotic cell death are underlying molecular mechanisms of non-alcoholic fatty liver disease. Redox Biology. 59. 102570–102570. 48 indexed citations
10.
Dodson, Matthew, Annadurai Anandhan, Cody J. Schmidlin, et al.. (2022). CHML is an NRF2 target gene that regulates mTOR function. Molecular Oncology. 16(8). 1714–1727. 2 indexed citations
11.
Wei, Juncheng, Bryan T. Harada, Dan Lü, et al.. (2021). HRD1-mediated METTL14 degradation regulates m6A mRNA modification to suppress ER proteotoxic liver disease. Molecular Cell. 81(24). 5052–5065.e6. 51 indexed citations
12.
Schmidlin, Cody J., Aryatara Shakya, Matthew Dodson, Eli Chapman, & Donna D. Zhang. (2021). The intricacies of NRF2 regulation in cancer. Seminars in Cancer Biology. 76. 110–119. 77 indexed citations
13.
Schmidlin, Cody J., Tian Wang, Matthew Dodson, Eli Chapman, & Donna D. Zhang. (2021). FAM129B‐dependent activation of NRF2 promotes an invasive phenotype in BRAF mutant melanoma cells. Molecular Carcinogenesis. 60(5). 331–341. 17 indexed citations
14.
Zhang, Donna D. & Eli Chapman. (2020). The role of natural products in revealing NRF2 function. Natural Product Reports. 37(6). 797–826. 91 indexed citations
15.
Liu, Pengfei, Matthew Dodson, Deyu Fang, Eli Chapman, & Donna D. Zhang. (2020). NRF2 negatively regulates primary ciliogenesis and hedgehog signaling. PLoS Biology. 18(2). e3000620–e3000620. 20 indexed citations
16.
Liu, Pengfei, Gang Luo, Matthew Dodson, et al.. (2020). The NRF2-LOC344887 signaling axis suppresses pulmonary fibrosis. Redox Biology. 38. 101766–101766. 38 indexed citations
17.
Schmidlin, Cody J., et al.. (2020). Activation of NRF2 by topical apocarotenoid treatment mitigates radiation-induced dermatitis. Redox Biology. 37. 101714–101714. 17 indexed citations
18.
Gao, Beixue, Qingfei Kong, Yana Zhang, et al.. (2017). The Histone Acetyltransferase Gcn5 Positively Regulates T Cell Activation. The Journal of Immunology. 198(10). 3927–3938. 36 indexed citations
19.
Tao, Shasha, Montserrat Rojo de la Vega, Eli Chapman, Aikseng Ooi, & Donna D. Zhang. (2017). The effects of NRF2 modulation on the initiation and progression of chemically and genetically induced lung cancer. Molecular Carcinogenesis. 57(2). 182–192. 108 indexed citations
20.
Tao, Shasha, Shue Wang, Seyed Javad Moghaddam, et al.. (2014). Oncogenic KRAS Confers Chemoresistance by Upregulating NRF2. Cancer Research. 74(24). 7430–7441. 241 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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