David J. Lynn

12.0k total citations · 3 hit papers
95 papers, 5.1k citations indexed

About

David J. Lynn is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, David J. Lynn has authored 95 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 26 papers in Immunology and 16 papers in Cancer Research. Recurrent topics in David J. Lynn's work include Gut microbiota and health (10 papers), Cancer-related molecular mechanisms research (9 papers) and Bioinformatics and Genomic Networks (9 papers). David J. Lynn is often cited by papers focused on Gut microbiota and health (10 papers), Cancer-related molecular mechanisms research (9 papers) and Bioinformatics and Genomic Networks (9 papers). David J. Lynn collaborates with scholars based in Australia, Ireland and Canada. David J. Lynn's co-authors include Fiona S. L. Brinkman, Amir Foroushani, Robert E. W. Hancock, Cliona O’Farrelly, Geoffrey L. Winsor, Matthew R. Laird, Raymond Lo, Karin Breuer, Carol Chen and Andrew T. Lloyd and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

David J. Lynn

93 papers receiving 5.0k citations

Hit Papers

InnateDB: systems biology of innate immunity and beyond—r... 2012 2026 2016 2021 2012 2022 2021 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David J. Lynn Australia 39 2.3k 1.2k 784 733 650 95 5.1k
Ying Zhang China 51 2.9k 1.3× 2.0k 1.7× 671 0.9× 1.9k 2.5× 445 0.7× 523 9.3k
Xiang Qin United States 46 4.7k 2.1× 689 0.6× 312 0.4× 1.7k 2.3× 918 1.4× 180 9.1k
Trine H. Mogensen Denmark 36 2.1k 0.9× 3.9k 3.3× 416 0.5× 1.3k 1.7× 438 0.7× 116 7.0k
Qing He China 32 984 0.4× 839 0.7× 418 0.5× 470 0.6× 327 0.5× 120 4.3k
Andreas M. Kaufmann Germany 48 2.8k 1.2× 2.7k 2.3× 946 1.2× 432 0.6× 631 1.0× 250 7.9k
Michael J. Coyne United States 37 4.6k 2.0× 378 0.3× 423 0.5× 877 1.2× 1.2k 1.8× 104 6.5k
Rui Luo China 35 1.0k 0.4× 1.0k 0.9× 253 0.3× 1.7k 2.3× 815 1.3× 169 4.1k
David J. Kelvin Canada 56 3.0k 1.3× 4.3k 3.7× 326 0.4× 2.0k 2.7× 543 0.8× 180 10.5k
Li Wu United States 48 3.4k 1.5× 3.6k 3.1× 721 0.9× 1.4k 1.9× 469 0.7× 185 9.0k
Ali A. Ashkar Canada 48 1.1k 0.5× 5.7k 4.9× 327 0.4× 753 1.0× 438 0.7× 156 8.2k

Countries citing papers authored by David J. Lynn

Since Specialization
Citations

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

Fields of papers citing papers by David J. Lynn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David J. Lynn

This figure shows the co-authorship network connecting the top 25 collaborators of David J. Lynn. A scholar is included among the top collaborators of David J. Lynn 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 J. Lynn. David J. Lynn 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.
Stevens, Hannah, James D. McFadyen, Natalie A. Mellett, et al.. (2025). Beyond platelet activation: dysregulated lipid metabolism in defining risk and pathophysiology of VITT. Research and Practice in Thrombosis and Haemostasis. 9(1). 102677–102677. 1 indexed citations
2.
Cakouros, Dimitrios, Feargal J. Ryan, David J. Lynn, et al.. (2025). DNA hydroxy methylases Tet1 and Tet2 regulate bone aging and bone marrow stromal cell metabolism through the IGF-1/mTOR signaling axis. Stem Cells. 43(8).
3.
Ryan, Feargal J., et al.. (2024). The role of the gut microbiota in regulating responses to vaccination: current knowledge and future directions. FEBS Journal. 292(6). 1480–1499. 4 indexed citations
4.
Rekima, Akila, Miriam A. Lynn, A.M. Middleton, et al.. (2024). Diet at birth is critical for healthy growth, independent of effects on the gut microbiota. Microbiome. 12(1). 139–139. 5 indexed citations
5.
Gillis, Joanna L., Chui Y. Mah, Swati Irani, et al.. (2023). Targeting hyaluronan-mediated motility receptor (HMMR) enhances response to androgen receptor signalling inhibitors in prostate cancer. British Journal of Cancer. 129(8). 1350–1361. 7 indexed citations
6.
Lynn, Miriam A., et al.. (2022). Protocol to assess the impact of early-life antibiotic exposure on murine longevity. STAR Protocols. 3(1). 101220–101220. 4 indexed citations
7.
Stevens, Natalie E., Feargal J. Ryan, Byron Brook, et al.. (2021). Immunisation with the BCG and DTPw vaccines induces different programs of trained immunity in mice. Vaccine. 40(11). 1594–1605. 8 indexed citations
8.
Blake, Stephen J., et al.. (2021). OX40‐targeted immune agonist antibodies induce potent antitumor immune responses without inducing liver damage in mice. FASEB BioAdvances. 3(10). 829–840. 2 indexed citations
9.
Blake, Stephen J., Feargal J. Ryan, José A. Caparrós‐Martín, et al.. (2021). The immunotoxicity, but not anti-tumor efficacy, of anti-CD40 and anti-CD137 immunotherapies is dependent on the gut microbiota. Cell Reports Medicine. 2(12). 100464–100464. 26 indexed citations
10.
Ryan, Feargal J., Jillian M. Carr, João M. Furtado, et al.. (2021). Zika Virus Infection of Human Iris Pigment Epithelial Cells. Frontiers in Immunology. 12. 644153–644153. 12 indexed citations
11.
Nassar, Zeyad D., Chui Y. Mah, Margaret M. Centenera, et al.. (2020). Fatty Acid Oxidation Is an Adaptive Survival Pathway Induced in Prostate Tumors by HSP90 Inhibition. Molecular Cancer Research. 18(10). 1500–1511. 19 indexed citations
13.
Qian, Xiaoyan, et al.. (2018). Network Visualization and Analysis of Spatially Aware Gene Expression Data with InsituNet. Cell Systems. 6(5). 626–630.e3. 11 indexed citations
14.
Lynn, David J. & Bali Pulendran. (2017). The potential of the microbiota to influence vaccine responses. Journal of Leukocyte Biology. 103(2). 225–231. 69 indexed citations
15.
16.
Lemay, Danielle G., David J. Lynn, William Martin, et al.. (2009). The bovine lactation genome: insights into the evolution of mammalian milk. Genome biology. 10(4). R43–R43. 148 indexed citations
17.
Freeman, Abigail R, David J. Lynn, Caitríona Murray, & Daniel G. Bradley. (2008). Detecting the effects of selection at the population level in six bovine immune genes. BMC Genetics. 9(1). 62–62. 8 indexed citations
18.
Levine, Ruth, et al.. (2004). Transforming a Clinical Clerkship with Team Learning. Teaching and Learning in Medicine. 16(3). 270–275. 165 indexed citations
19.
Lynn, David J., Andrew T. Lloyd, Mario A. Fares, & Cliona O’Farrelly. (2004). Evidence of Positively Selected Sites in Mammalian α-Defensins. Molecular Biology and Evolution. 21(5). 819–827. 58 indexed citations
20.
Lynn, David J., et al.. (2003). Bioinformatics: implications for medical research and clinical practice.. PubMed. 26(2). 70–4. 1 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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