David G. Burbee

1.6k total citations · 1 hit paper
9 papers, 1.4k citations indexed

About

David G. Burbee is a scholar working on Molecular Biology, Cell Biology and Genetics. According to data from OpenAlex, David G. Burbee has authored 9 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Cell Biology and 2 papers in Genetics. Recurrent topics in David G. Burbee's work include Cancer-related gene regulation (3 papers), RNA modifications and cancer (2 papers) and Epigenetics and DNA Methylation (2 papers). David G. Burbee is often cited by papers focused on Cancer-related gene regulation (3 papers), RNA modifications and cancer (2 papers) and Epigenetics and DNA Methylation (2 papers). David G. Burbee collaborates with scholars based in United States, Sweden and United Kingdom. David G. Burbee's co-authors include Masashi Kondo, Sabine Zöchbauer‐Müller, Farida Latif, Éva Forgács, J D Minna, Sara Milchgrub, Shinichi Toyooka, Eugene R. Zabarovsky, Michael A. White and Bo Gao and has published in prestigious journals such as Science, Nature Genetics and JNCI Journal of the National Cancer Institute.

In The Last Decade

David G. Burbee

9 papers receiving 1.3k citations

Hit Papers

Epigenetic Inactivation of RASSF1A in Lung and Breast Can... 2001 2026 2009 2017 2001 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David G. Burbee United States 8 1.1k 212 212 185 180 9 1.4k
Mari Kuraguchi United States 14 905 0.9× 65 0.3× 372 1.8× 46 0.2× 164 0.9× 17 1.3k
Brian P. Cook United States 11 562 0.5× 118 0.6× 258 1.2× 42 0.2× 153 0.8× 12 929
Hiu Wing Cheung China 16 876 0.8× 123 0.6× 457 2.2× 141 0.8× 104 0.6× 36 1.3k
Guiyang Jiang China 21 899 0.8× 261 1.2× 310 1.5× 124 0.7× 70 0.4× 78 1.3k
Yongyou Zhang China 19 961 0.9× 253 1.2× 236 1.1× 451 2.4× 99 0.6× 29 1.4k
Benoit deCrombrugghe United States 14 721 0.7× 52 0.2× 167 0.8× 61 0.3× 260 1.4× 17 993
Jung Hwan Yoon South Korea 21 698 0.7× 186 0.9× 276 1.3× 49 0.3× 65 0.4× 63 1.2k
Naoko Ohtani‐Fujita Japan 14 1.2k 1.2× 61 0.3× 586 2.8× 54 0.3× 151 0.8× 17 1.5k
Robert T. Pu United States 20 1.6k 1.5× 123 0.6× 346 1.6× 727 3.9× 113 0.6× 40 2.0k
Judy Grover Canada 15 378 0.4× 28 0.1× 90 0.4× 308 1.7× 124 0.7× 23 779

Countries citing papers authored by David G. Burbee

Since Specialization
Citations

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

Fields of papers citing papers by David G. Burbee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David G. Burbee

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

All Works

9 of 9 papers shown
1.
Lea, Jayanthi, Raheela Ashfaq, Sabeeha Muneer, et al.. (2004). Understanding the Mechanisms of FHIT Inactivation in Cervical Cancer for Biomarker Development. Journal of the Society for Gynecologic Investigation. 11(5). 329–337. 9 indexed citations
2.
Ji, Lin, Masahiko Nishizaki, Boning Gao, et al.. (2002). Expression of several genes in the human chromosome 3p21.3 homozygous deletion region by an adenovirus vector results in tumor suppressor activities in vitro and in vivo.. PubMed. 62(9). 2715–20. 153 indexed citations
3.
Burbee, David G., Éva Forgács, Sabine Zöchbauer‐Müller, et al.. (2001). Epigenetic Inactivation of RASSF1A in Lung and Breast Cancers and Malignant Phenotype Suppression. JNCI Journal of the National Cancer Institute. 93(9). 691–699. 659 indexed citations breakdown →
4.
Wang, Eric, et al.. (1998). Genomic organization and cloning of the human homologue of murine Sipa-1. Gene. 214(1-2). 215–221. 1 indexed citations
5.
Li, Ping, K. Küpfer, Christopher Davies, et al.. (1997). PRIMO: A Primer Design Program That Applies Base Quality Statistics for Automated Large-Scale DNA Sequencing. Genomics. 40(3). 476–485. 47 indexed citations
6.
Stickens, Dominique, Gregory A. Clines, David G. Burbee, et al.. (1996). The EXT2 multiple exostoses gene defines a family of putative tumour suppressor genes. Nature Genetics. 14(1). 25–32. 271 indexed citations
7.
Hoekstra, Merl F., et al.. (1991). HRR25, a Putative Protein Kinase from Budding Yeast: Association with Repair of Damaged DNA. Science. 253(5023). 1031–1034. 162 indexed citations
8.
Roberts, Jeffrey W., et al.. (1982). A brief consideration of the SOS inducing signal. Biochimie. 64(8-9). 805–807. 27 indexed citations
9.
Sparnins, Velta L., David G. Burbee, & S. Dagley. (1979). Catabolism of L-tyrosine in Trichosporon cutaneum. Journal of Bacteriology. 138(2). 425–430. 49 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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