David C. James

9.8k total citations · 2 hit papers
127 papers, 7.3k citations indexed

About

David C. James is a scholar working on Molecular Biology, Genetics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, David C. James has authored 127 papers receiving a total of 7.3k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Molecular Biology, 24 papers in Genetics and 23 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in David C. James's work include Viral Infectious Diseases and Gene Expression in Insects (57 papers), Virus-based gene therapy research (23 papers) and Monoclonal and Polyclonal Antibodies Research (22 papers). David C. James is often cited by papers focused on Viral Infectious Diseases and Gene Expression in Insects (57 papers), Virus-based gene therapy research (23 papers) and Monoclonal and Polyclonal Antibodies Research (22 papers). David C. James collaborates with scholars based in United Kingdom, Australia and United States. David C. James's co-authors include D. A. Brewerton, Anne Nicholls, F. Dudley Hart, R D Sturrock, D. Walters, Nigel Jenkins, Diane M. Dinnis, Andrew J. Racher, Raj Parekh and Yuan Zheng and has published in prestigious journals such as Nature, The Lancet and Nucleic Acids Research.

In The Last Decade

David C. James

127 papers receiving 6.8k citations

Hit Papers

ANKYLOSING SPONDYLITIS AND HL-A 27 1973 2026 1990 2008 1973 1973 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David C. James United Kingdom 41 3.9k 2.1k 1.7k 1.3k 1.2k 127 7.3k
Tatsuji Yasuda Japan 38 2.7k 0.7× 1.1k 0.5× 1.7k 1.0× 806 0.6× 460 0.4× 174 5.5k
William J. Koopman United States 47 1.8k 0.5× 2.4k 1.1× 3.8k 2.2× 2.1k 1.5× 455 0.4× 178 8.0k
Bernard J. Scallon United States 28 1.3k 0.3× 633 0.3× 1.5k 0.8× 1.0k 0.8× 674 0.6× 38 3.9k
Amy S. Rosenberg United States 35 2.5k 0.6× 733 0.3× 1.6k 0.9× 1.3k 0.9× 362 0.3× 84 5.5k
Ger J.M. Pruijn Netherlands 56 6.1k 1.6× 3.0k 1.4× 2.7k 1.6× 2.1k 1.5× 804 0.7× 189 11.3k
Thomas Winkler Germany 55 3.4k 0.9× 1.1k 0.5× 4.6k 2.7× 1.1k 0.8× 829 0.7× 210 10.0k
Peter A. Kiener United States 59 3.8k 1.0× 970 0.5× 4.4k 2.5× 1.7k 1.3× 410 0.3× 124 10.6k
Mark J. Mamula United States 40 1.2k 0.3× 1.2k 0.6× 3.5k 2.0× 985 0.7× 720 0.6× 86 5.8k
Ralph B. Arlinghaus United States 51 5.4k 1.4× 1.3k 0.6× 1.1k 0.7× 585 0.4× 1.3k 1.1× 250 10.7k
P. J. Lachmann United Kingdom 50 1.6k 0.4× 922 0.4× 4.6k 2.6× 1.2k 0.9× 630 0.5× 201 8.0k

Countries citing papers authored by David C. James

Since Specialization
Citations

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

Fields of papers citing papers by David C. James

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David C. James

This figure shows the co-authorship network connecting the top 25 collaborators of David C. James. A scholar is included among the top collaborators of David C. James 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 C. James. David C. James 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.
Liu, Ping, et al.. (2024). Molecular design of controllable recombinant adeno‐associated virus (AAV) expression systems for enhanced vector production. Biotechnology Journal. 19(6). e2300685–e2300685. 5 indexed citations
2.
Harris, Claire, Rebecca E. Williams, Olalekan Daramola, et al.. (2023). CHO synthetic promoters improve expression and product quality of biotherapeutic proteins. Biotechnology Progress. 39(5). e3348–e3348. 2 indexed citations
3.
Cartwright, Joseph A., et al.. (2023). Directed evolution of biomass intensive CHO cells by adaptation to sub-physiological temperature. Metabolic Engineering. 81. 53–69. 2 indexed citations
4.
Liu, Ping, et al.. (2022). Engineering of the CMV promoter for controlled expression of recombinant genes in HEK293 cells. Biotechnology Journal. 17(8). e2200062–e2200062. 16 indexed citations
5.
Ross, Paul J., Harpreet Wasan, Daniel Croagh, et al.. (2021). Results of a single-arm pilot study of 32P microparticles in unresectable locally advanced pancreatic adenocarcinoma with gemcitabine/nab-paclitaxel or FOLFIRINOX chemotherapy. ESMO Open. 7(1). 100356–100356. 13 indexed citations
6.
Mercer, Andrew C., et al.. (2021). Design of synthetic promoters for controlled expression of therapeutic genes in retinal pigment epithelial cells. Biotechnology and Bioengineering. 118(5). 2001–2015. 14 indexed citations
7.
Maisuria, Sheetal, Adam Brown, Kang Lan Tee, et al.. (2020). Production of trimeric SARS‐CoV‐2 spike protein by CHO cells for serological COVID‐19 testing. Biotechnology and Bioengineering. 118(2). 1013–1021. 23 indexed citations
8.
Coss, Karen P., et al.. (2020). Cell function profiling to assess clone stability. Biotechnology and Bioengineering. 117(7). 2295–2299. 4 indexed citations
9.
Brown, Adam, Christina Alves, Yizhou Zhou, et al.. (2019). CHO genome mining for synthetic promoter design. Journal of Biotechnology. 294. 1–13. 14 indexed citations
10.
Brown, Adam, et al.. (2018). Whole synthetic pathway engineering of recombinant protein production. Biotechnology and Bioengineering. 116(2). 375–387. 19 indexed citations
11.
Hatton, Diane, et al.. (2018). Control of amino acid transport into Chinese hamster ovary cells. Biotechnology and Bioengineering. 115(12). 2908–2929. 12 indexed citations
12.
Brown, Adam & David C. James. (2015). Precision control of recombinant gene transcription for CHO cell synthetic biology. Biotechnology Advances. 34(5). 492–503. 25 indexed citations
13.
Davies, Sarah L., et al.. (2012). Functional heterogeneity and heritability in CHO cell populations. Biotechnology and Bioengineering. 110(1). 260–274. 78 indexed citations
14.
O’Callaghan, Peter M., et al.. (2010). Cell line‐specific control of recombinant monoclonal antibody production by CHO cells. Biotechnology and Bioengineering. 106(6). 938–951. 88 indexed citations
15.
Smales, C. Mark, et al.. (2007). Transient Gene Expression Levels from Multigene Expression Vectors. Biotechnology Progress. 23(2). 435–443. 23 indexed citations
16.
Smales, C. Mark & David C. James. (2005). Therapeutic proteins : methods and protocols. Humana Press eBooks. 67 indexed citations
17.
Willingham, Mark C., Giulia Caron, David C. James, C. Mark Smales, & Gary K. Robinson. (2005). Monitoring changes in nisin susceptibility of Listeria monocytogenes Scott A as an indicator of growth phase using FACS. Journal of Microbiological Methods. 66(1). 43–55. 12 indexed citations
18.
Willingham, Mark C., David C. James, Gary K. Robinson, & C. Mark Smales. (2004). Global changes in gene expression observed at the transition from growth to stationary phase in Listeria monocytogenes ScottA batch culture. PROTEOMICS. 4(1). 123–135. 11 indexed citations
20.
Patel, Ashvin, et al.. (2002). Rapid monitoring of recombinant protein products: a comparison of current technologies. Trends in biotechnology. 20(4). 149–156. 92 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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