Benjamin Caleb

1.8k total citations
8 papers, 827 citations indexed

About

Benjamin Caleb is a scholar working on Molecular Biology, Cell Biology and Immunology and Allergy. According to data from OpenAlex, Benjamin Caleb has authored 8 papers receiving a total of 827 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 4 papers in Cell Biology and 3 papers in Immunology and Allergy. Recurrent topics in Benjamin Caleb's work include Cell Adhesion Molecules Research (3 papers), Proteoglycans and glycosaminoglycans research (3 papers) and Cancer therapeutics and mechanisms (2 papers). Benjamin Caleb is often cited by papers focused on Cell Adhesion Molecules Research (3 papers), Proteoglycans and glycosaminoglycans research (3 papers) and Cancer therapeutics and mechanisms (2 papers). Benjamin Caleb collaborates with scholars based in United States and United Kingdom. Benjamin Caleb's co-authors include John J. Castellot, Thomas C. Wright, Morris J. Karnovsky, Kent Wong, David F. Albertini, Brian Herman, Richard L. Hoover, Didier Letourneur, Laurie Pukac and Candice L. Horn and has published in prestigious journals such as Journal of Cellular Physiology, Molecular Cancer Therapeutics and ACS Medicinal Chemistry Letters.

In The Last Decade

Benjamin Caleb

8 papers receiving 807 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Benjamin Caleb United States 7 526 288 232 149 121 8 827
Xavier Canron France 11 614 1.2× 117 0.4× 285 1.2× 136 0.9× 102 0.8× 12 879
Mark G. Slomiany United States 14 780 1.5× 344 1.2× 364 1.6× 251 1.7× 77 0.6× 18 1.2k
Yin Xu United States 11 600 1.1× 169 0.6× 230 1.0× 173 1.2× 87 0.7× 15 858
Christophe Schneider France 19 617 1.2× 131 0.5× 307 1.3× 242 1.6× 94 0.8× 41 1.1k
Romina Dossi Italy 10 453 0.9× 103 0.4× 201 0.9× 158 1.1× 126 1.0× 11 708
Dai Nakashima Japan 22 832 1.6× 178 0.6× 346 1.5× 328 2.2× 43 0.4× 49 1.2k
M Kan United States 12 759 1.4× 283 1.0× 117 0.5× 125 0.8× 69 0.6× 18 954
Kym L. Stanley Australia 9 588 1.1× 146 0.5× 310 1.3× 123 0.8× 134 1.1× 10 893
Elvira V. Grigorieva Russia 18 466 0.9× 318 1.1× 115 0.5× 174 1.2× 64 0.5× 45 715
Maya Zigler United States 17 565 1.1× 93 0.3× 300 1.3× 184 1.2× 109 0.9× 23 961

Countries citing papers authored by Benjamin Caleb

Since Specialization
Citations

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

Fields of papers citing papers by Benjamin Caleb

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benjamin Caleb

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

All Works

8 of 8 papers shown
1.
Cook, Andrew S., Kiyoyuki Omoto, Jiyuan Ke, et al.. (2020). Discovery of Aminopyrazole Derivatives as Potent Inhibitors of Wild-Type and Gatekeeper Mutant FGFR2 and 3. ACS Medicinal Chemistry Letters. 12(1). 93–98. 16 indexed citations
2.
Zabludoff, Sonya, Chun Deng, Michael Grondine, et al.. (2008). AZD7762, a novel checkpoint kinase inhibitor, drives checkpoint abrogation and potentiates DNA-targeted therapies. Molecular Cancer Therapeutics. 7(9). 2955–2966. 333 indexed citations
3.
Ashwell, Susan, Benjamin Caleb, Chun Deng, et al.. (2007). Preclinical identification of AZD7762, a novel, potent and selective inhibitor of Checkpoint kinases. Molecular Cancer Therapeutics. 6. 3 indexed citations
4.
Caleb, Benjamin, Mitchell Hardenbrook, Van Cherington, & John J. Castellot. (1996). Isolation of vascular smooth muscle cell cultures with altered responsiveness to the antiproliferative effect of heparin. Journal of Cellular Physiology. 167(2). 185–195. 14 indexed citations
5.
Letourneur, Didier, Benjamin Caleb, & John J. Castellot. (1995). Heparin binding, internalization, and metabolism in vascular smooth muscle cells: II. Degradation and secretion in sensitive and resistant cells. Journal of Cellular Physiology. 165(3). 687–695. 26 indexed citations
6.
Letourneur, Didier, Benjamin Caleb, & John J. Castellot. (1995). Heparin binding, internalization, and metabolism in vascular smooth muscle cells: I. Upregulation of heparin binding correlates with antiproliferative activity. Journal of Cellular Physiology. 165(3). 676–686. 60 indexed citations
7.
Pukac, Laurie, John J. Castellot, Thomas C. Wright, Benjamin Caleb, & Morris J. Karnovsky. (1990). Heparin inhibits c-fos and c-myc mRNA expression in vascular smooth muscle cells.. PubMed. 1(5). 435–443. 116 indexed citations
8.
Castellot, John J., Kent Wong, Brian Herman, et al.. (1985). Binding and internalization of heparin by vascular smooth muscle cells. Journal of Cellular Physiology. 124(1). 13–20. 259 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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