Greg Dean

498 total citations
8 papers, 394 citations indexed

About

Greg Dean is a scholar working on Molecular Biology, Oncology and Genetics. According to data from OpenAlex, Greg Dean has authored 8 papers receiving a total of 394 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 2 papers in Oncology and 2 papers in Genetics. Recurrent topics in Greg Dean's work include Viral Infectious Diseases and Gene Expression in Insects (4 papers), Signaling Pathways in Disease (3 papers) and Protein purification and stability (3 papers). Greg Dean is often cited by papers focused on Viral Infectious Diseases and Gene Expression in Insects (4 papers), Signaling Pathways in Disease (3 papers) and Protein purification and stability (3 papers). Greg Dean collaborates with scholars based in United Kingdom and United States. Greg Dean's co-authors include Olalekan Daramola, Ray Field, Diane Hatton, David C. James, Leon P. Pybus, Andrew Smith, William E. Holmes, Jessica Stevenson, Gary Pettman and Jin Xu and has published in prestigious journals such as Journal of Biological Chemistry, Annals of the New York Academy of Sciences and Molecular Pharmacology.

In The Last Decade

Greg Dean

8 papers receiving 373 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Greg Dean United Kingdom	7	343	128	69	67	58	8	394
Rama Thimmapaya United States	9	344 1.0×	64 0.5×	19 0.3×	48 0.7×	58 1.0×	12	432
Michelle K.M. Chow Australia	10	299 0.9×	143 1.1×	16 0.2×	17 0.3×	30 0.5×	14	394
Ekaterina Boudanova United States	10	185 0.5×	82 0.6×	100 1.4×	14 0.2×	14 0.2×	12	303
Christopher D. Heger United States	8	243 0.7×	55 0.4×	42 0.6×	22 0.3×	14 0.2×	16	375
Isabelle Breloy Germany	10	311 0.9×	44 0.3×	18 0.3×	32 0.5×	37 0.6×	12	364
L. van der Voorn Netherlands	6	384 1.1×	88 0.7×	8 0.1×	109 1.6×	20 0.3×	8	466
Samuel C. Griffiths United Kingdom	8	224 0.7×	34 0.3×	24 0.3×	57 0.9×	4 0.1×	14	330
M Philip United States	7	264 0.8×	99 0.8×	15 0.2×	104 1.6×	11 0.2×	8	345
Rosa Ventrella United States	10	171 0.5×	35 0.3×	26 0.4×	51 0.8×	19 0.3×	17	275
Alan D. Andrews United States	9	459 1.3×	38 0.3×	17 0.2×	56 0.8×	48 0.8×	11	541

Countries citing papers authored by Greg Dean

Since Specialization

Citations

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

Fields of papers citing papers by Greg Dean

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Greg Dean

This figure shows the co-authorship network connecting the top 25 collaborators of Greg Dean. A scholar is included among the top collaborators of Greg Dean 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 Greg Dean. Greg Dean is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

Cartwright, Joseph A., Guglielmo Rosignoli, Claire Harris, et al.. (2020). A platform for context-specific genetic engineering of recombinant protein production by CHO cells. Journal of Biotechnology. 312. 11–22. 20 indexed citations

Pybus, Leon P., David C. James, Greg Dean, et al.. (2013). Predicting the expression of recombinant monoclonal antibodies in Chinese hamster ovary cells based on sequence features of the CDR3 domain. Biotechnology Progress. 30(1). 188–197. 20 indexed citations

Daramola, Olalekan, Jessica Stevenson, Greg Dean, et al.. (2013). A high‐yielding CHO transient system: Coexpression of genes encoding EBNA‐1 and GS enhances transient protein expression. Biotechnology Progress. 30(1). 132–141. 95 indexed citations

Pybus, Leon P., Greg Dean, Andrew Smith, et al.. (2013). Model‐directed engineering of “difficult‐to‐express” monoclonal antibody production by Chinese hamster ovary cells. Biotechnology and Bioengineering. 111(2). 372–385. 88 indexed citations

Dean, Greg, David A. Young, Dylan R. Edwards, & Ian M. Clark. (2000). The human TIMP-1 gene contains repressive elements within the promoter and intron 1. Journal of Biological Chemistry. 6 indexed citations

Dean, Greg, David A. Young, Dylan R. Edwards, & Ian M. Clark. (2000). The Human Tissue Inhibitor of Metalloproteinases (TIMP)-1 Gene Contains Repressive Elements within the Promoter and Intron 1. Journal of Biological Chemistry. 275(42). 32664–32671. 31 indexed citations

Dean, Greg & Ian M. Clark. (1999). Transcriptional Regulation of the Human Tissue Inhibitor of Metalloproteinases‐1: Mapping Transcriptional Control in Intron‐1. Annals of the New York Academy of Sciences. 878(1). 510–511. 5 indexed citations

Cheng, Jun, Jin Xu, Grace C. Rossi, et al.. (1995). Cloning and functional characterization through antisense mapping of a kappa 3-related opioid receptor.. Molecular Pharmacology. 47(6). 1180–1188. 129 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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