David Dornan

5.2k total citations · 2 hit papers
38 papers, 3.1k citations indexed

About

David Dornan is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, David Dornan has authored 38 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Oncology, 18 papers in Molecular Biology and 12 papers in Immunology. Recurrent topics in David Dornan's work include Cancer-related Molecular Pathways (10 papers), Monoclonal and Polyclonal Antibodies Research (9 papers) and Ubiquitin and proteasome pathways (7 papers). David Dornan is often cited by papers focused on Cancer-related Molecular Pathways (10 papers), Monoclonal and Polyclonal Antibodies Research (9 papers) and Ubiquitin and proteasome pathways (7 papers). David Dornan collaborates with scholars based in United States, United Kingdom and Denmark. David Dornan's co-authors include Vishva M. Dixit, Harumi Shimizu, Ingrid E. Wertz, David Arnott, Ted R. Hupp, Karen O’Rourke, Hartmut Koeppen, Patrick J. Dowd, Gretchen Frantz and Yue Peng and has published in prestigious journals such as Nature, Science and Journal of Biological Chemistry.

In The Last Decade

David Dornan

36 papers receiving 3.1k citations

Hit Papers

The ubiquitin ligase COP1 is a critical negative regulato... 2004 2026 2011 2018 2004 2009 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Dornan United States 20 2.5k 1.4k 816 288 281 38 3.1k
Mu‐Shui Dai United States 30 3.1k 1.2× 1.5k 1.1× 656 0.8× 301 1.0× 240 0.9× 62 3.6k
Karsten Zieger Denmark 14 2.4k 1.0× 1.1k 0.8× 788 1.0× 204 0.7× 324 1.2× 22 3.2k
Zuzana Hořejšı́ United Kingdom 16 3.3k 1.3× 1.6k 1.1× 674 0.8× 278 1.0× 489 1.7× 19 3.8k
Panayotis Zacharatos Greece 17 2.5k 1.0× 1.2k 0.9× 522 0.6× 173 0.6× 381 1.4× 21 3.1k
Torben F. Ørntoft Denmark 13 2.5k 1.0× 1.5k 1.1× 741 0.9× 191 0.7× 338 1.2× 20 3.6k
Richard A. DiTullio United States 9 2.8k 1.1× 1.4k 0.9× 604 0.7× 134 0.5× 408 1.5× 9 3.1k
Gareth L. Bond United Kingdom 23 2.6k 1.0× 2.2k 1.5× 825 1.0× 164 0.6× 278 1.0× 41 3.6k
Alexey V. Ivanov United States 24 2.2k 0.9× 744 0.5× 471 0.6× 506 1.8× 185 0.7× 50 2.7k
Mark K. Saville United Kingdom 23 2.7k 1.1× 1.9k 1.3× 490 0.6× 233 0.8× 362 1.3× 33 3.2k
Nabil Chehab United States 14 2.5k 1.0× 1.6k 1.1× 517 0.6× 113 0.4× 354 1.3× 17 3.0k

Countries citing papers authored by David Dornan

Since Specialization
Citations

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

Fields of papers citing papers by David Dornan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Dornan

This figure shows the co-authorship network connecting the top 25 collaborators of David Dornan. A scholar is included among the top collaborators of David Dornan 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 David Dornan. David Dornan 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.
2.
Kenkel, Justin A., Po Y. Ho, Sameera Kongara, et al.. (2021). 862 Dectin-2, a novel target for tumor macrophage reprogramming in cancer immunotherapy. Regular and Young Investigator Award Abstracts. A903–A903.
3.
Palermo, Giuseppe, Daniela Maisel, Martin Barrett, et al.. (2015). Gene expression of INPP5F as an independent prognostic marker in fludarabine-based therapy of chronic lymphocytic leukemia. Blood Cancer Journal. 5(10). e353–e353. 5 indexed citations
4.
Bager, Cecilie L., Nicholas Willumsen, Diana Julie Leeming, et al.. (2015). Collagen degradation products measured in serum can separate ovarian and breast cancer patients from healthy controls: A preliminary study. Cancer Biomarkers. 15(6). 783–788. 57 indexed citations
5.
Hwang, Michael S., Nancy Yu, Yue Peng, et al.. (2013). miR-221/222 Targets Adiponectin Receptor 1 to Promote the Epithelial-to-Mesenchymal Transition in Breast Cancer. PLoS ONE. 8(6). e66502–e66502. 115 indexed citations
6.
Willumsen, Nicholas, Cecilie L. Bager, Diana Julie Leeming, et al.. (2013). Extracellular matrix specific protein fingerprints measured in serum can separate pancreatic cancer patients from healthy controls. BMC Cancer. 13(1). 554–554. 49 indexed citations
7.
Shi, Xiaoyan & David Dornan. (2012). To respond or not to respond to CD40 agonism: That is the prediction. OncoImmunology. 1(1). 83–85. 4 indexed citations
10.
Dornan, David & Jeff Settleman. (2010). Cancer: miRNA Addiction — Depending On Life's Little Things. Current Biology. 20(18). R812–R813. 6 indexed citations
11.
Schwickart, Martin, Xiaodong Huang, Jennie R. Lill, et al.. (2009). Deubiquitinase USP9X stabilizes MCL1 and promotes tumour cell survival. Nature. 463(7277). 103–107. 510 indexed citations breakdown →
12.
Qing, Jing, Xiangnan Du, Yongmei Chen, et al.. (2009). Antibody-based targeting of FGFR3 in bladder carcinoma and t(4;14)-positive multiple myeloma in mice. Journal of Clinical Investigation. 119(5). 1216–1229. 197 indexed citations
13.
Burington, Bart, Thomas Januario, Jeffrey Lau, et al.. (2008). CD40 pathway activation status and germinal B-cell identity are predictive of response to anti-CD40 (SGN-40) in preclinical NHL models. Clinical Cancer Research. 14. 1 indexed citations
14.
Polson, Andrew G., Fiona Bennett, Yvonne Chen, et al.. (2008). Development and Therapeutic Potential of an Anti-CD79b Antibody-Drug Conjugate, Anti-CD79b-Vc-MMAE, for the Treatment of Non-Hodgkin’s Lymphoma. Blood. 112(11). 2618–2618. 4 indexed citations
15.
Wertz, Ingrid E., Karen O’Rourke, Zemin Zhang, et al.. (2004). Human De-Etiolated-1 Regulates c-Jun by Assembling a CUL4A Ubiquitin Ligase. Science. 303(5662). 1371–1374. 301 indexed citations
16.
Dornan, David, et al.. (2004). Interferon Regulatory Factor 1 Binding to p300 Stimulates DNA-Dependent Acetylation of p53. Molecular and Cellular Biology. 24(22). 10083–10098. 65 indexed citations
17.
Dornan, David, Ingrid E. Wertz, Harumi Shimizu, et al.. (2004). The ubiquitin ligase COP1 is a critical negative regulator of p53. Nature. 429(6987). 86–92. 568 indexed citations breakdown →
18.
Dornan, David, Harumi Shimizu, Neil D. Perkins, & Ted R. Hupp. (2003). DNA-dependent Acetylation of p53 by the Transcription Coactivator p300. Journal of Biological Chemistry. 278(15). 13431–13441. 92 indexed citations
19.
Shimizu, Harumi, Lindsay Burch, Amanda Smith, et al.. (2002). The Conformationally Flexible S9–S10 Linker Region in the Core Domain of p53 Contains a Novel MDM2 Binding Site Whose Mutation Increases Ubiquitination of p53 in Vivo. Journal of Biological Chemistry. 277(32). 28446–28458. 99 indexed citations
20.
Kelleher, Michael, et al.. (2002). Regulation of the IRF-1 tumour modifier during the response to genotoxic stress involves an ATM-dependent signalling pathway. Oncogene. 21(51). 7776–7785. 79 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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