C.J. Apolito

1.2k total citations · 1 hit paper
8 papers, 967 citations indexed

About

C.J. Apolito is a scholar working on Molecular Biology, Clinical Biochemistry and Genetics. According to data from OpenAlex, C.J. Apolito has authored 8 papers receiving a total of 967 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 2 papers in Clinical Biochemistry and 2 papers in Genetics. Recurrent topics in C.J. Apolito's work include Estrogen and related hormone effects (2 papers), Mitochondrial Function and Pathology (2 papers) and Viral Infectious Diseases and Gene Expression in Insects (2 papers). C.J. Apolito is often cited by papers focused on Estrogen and related hormone effects (2 papers), Mitochondrial Function and Pathology (2 papers) and Viral Infectious Diseases and Gene Expression in Insects (2 papers). C.J. Apolito collaborates with scholars based in United States and United Kingdom. C.J. Apolito's co-authors include Randy K. Bledsoe, Millard H. Lambert, Kenneth H. Pearce, Timothy M. Willson, Eugene L. Stewart, Thomas B. Stanley, Derek J. Parks, Thomas G. Consler, Valerie G. Montana and David D. McKee and has published in prestigious journals such as Cell, Journal of Biological Chemistry and Genomics.

In The Last Decade

C.J. Apolito

8 papers receiving 944 citations

Hit Papers

Crystal Structure of the ... 2002 2026 2010 2018 2002 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
C.J. Apolito United States 7 567 495 343 106 92 8 967
Debra F. Skafar United States 20 574 1.0× 417 0.8× 173 0.5× 221 2.1× 80 0.9× 36 1.0k
P E Strömstedt United States 12 620 1.1× 665 1.3× 322 0.9× 111 1.0× 129 1.4× 15 1.2k
Masaaki Kagimoto United States 13 228 0.4× 566 1.1× 392 1.1× 103 1.0× 18 0.2× 24 963
Esther Y. Chao United States 11 165 0.3× 546 1.1× 87 0.3× 80 0.8× 39 0.4× 15 881
Gilles Leblanc Canada 14 606 1.1× 493 1.0× 781 2.3× 55 0.5× 32 0.3× 14 1.3k
Qi-Hui Gong United States 9 153 0.3× 589 1.2× 139 0.4× 161 1.5× 32 0.3× 10 962
Jacek Ostrowski United States 15 256 0.5× 569 1.1× 151 0.4× 63 0.6× 52 0.6× 24 838
S P Tam Canada 16 162 0.3× 383 0.8× 202 0.6× 130 1.2× 62 0.7× 20 801
Cathleen D. Valentine United States 11 514 0.9× 499 1.0× 98 0.3× 175 1.7× 52 0.6× 12 775
J Picado-Leonard United States 9 454 0.8× 778 1.6× 746 2.2× 53 0.5× 29 0.3× 9 1.3k

Countries citing papers authored by C.J. Apolito

Since Specialization
Citations

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

Fields of papers citing papers by C.J. Apolito

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of C.J. Apolito

This figure shows the co-authorship network connecting the top 25 collaborators of C.J. Apolito. A scholar is included among the top collaborators of C.J. Apolito 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 C.J. Apolito. C.J. Apolito 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.
Koury, Stephen T., et al.. (2007). Differential gene expression during terminal erythroid differentiation. Genomics. 90(5). 574–582. 8 indexed citations
2.
Bledsoe, Randy K., Kevin P. Madauss, Jason A. Holt, et al.. (2005). A Ligand-mediated Hydrogen Bond Network Required for the Activation of the Mineralocorticoid Receptor. Journal of Biological Chemistry. 280(35). 31283–31293. 166 indexed citations
3.
Bledsoe, Randy K., Valerie G. Montana, Thomas B. Stanley, et al.. (2002). Crystal Structure of the Glucocorticoid Receptor Ligand Binding Domain Reveals a Novel Mode of Receptor Dimerization and Coactivator Recognition. Cell. 110(1). 93–105. 652 indexed citations breakdown →
4.
Worley, Jennings F., et al.. (2002). Titration of K ATP Channel Expression in Mammalian Cells Utilizing Recombinant Baculovirus Transduction. 8(2). 99–111. 18 indexed citations
5.
Worley, Jennings F., et al.. (2002). Titration of K ATP Channel Expression in Mammalian Cells Utilizing Recombinant Baculovirus Transduction. PubMed. 8(2). 99–111. 28 indexed citations
6.
Taggart, R. Thomas, et al.. (1999). Novel mutations associated with carnitine palmitoyltransferase II deficiency. Human Mutation. 13(3). 210–220. 71 indexed citations
7.
Taggart, R.T., David Smail, C.J. Apolito, & Georgirene D. Vladutiu. (1999). Novel mutations associated with carnitine palmitoyltransferase II deficiency. Human Mutation. 13(3). 210–210. 4 indexed citations
8.
Greenberg, Jay, Lon Phan, Zhenyu Gu, et al.. (1998). Nip1p Associates with 40 S Ribosomes and the Prt1p Subunit of Eukaryotic Initiation Factor 3 and Is Required for Efficient Translation Initiation. Journal of Biological Chemistry. 273(36). 23485–23494. 20 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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