Peter Cherbas

11.6k total citations · 3 hit papers
55 papers, 5.5k citations indexed

About

Peter Cherbas is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Genetics. According to data from OpenAlex, Peter Cherbas has authored 55 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Cellular and Molecular Neuroscience, 26 papers in Molecular Biology and 13 papers in Genetics. Recurrent topics in Peter Cherbas's work include Neurobiology and Insect Physiology Research (32 papers), Animal Behavior and Reproduction (10 papers) and Insect Utilization and Effects (9 papers). Peter Cherbas is often cited by papers focused on Neurobiology and Insect Physiology Research (32 papers), Animal Behavior and Reproduction (10 papers) and Insect Utilization and Effects (9 papers). Peter Cherbas collaborates with scholars based in United States, Italy and United Kingdom. Peter Cherbas's co-authors include Lucy Cherbas, James W. Truman, Lynn M. Riddiford, Michael Bender, William A. Segraves, David S. Hogness, William S. Talbot, Michael R. Koelle, Ilaria Rebay and R.J. Fleming and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Peter Cherbas

54 papers receiving 5.4k citations

Hit Papers

The drosophila EcR gene encodes an ecdysone receptor, a n... 1991 2026 2002 2014 1991 1993 1991 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Cherbas United States 31 3.0k 2.9k 1.3k 1.3k 894 55 5.5k
Lucy Cherbas United States 27 1.9k 0.6× 2.1k 0.7× 868 0.7× 818 0.6× 660 0.7× 41 3.8k
James W. Fristrom United States 39 2.5k 0.8× 2.9k 1.0× 1.4k 1.1× 1.2k 1.0× 843 0.9× 89 5.1k
Marek Jindra Czechia 31 2.6k 0.9× 1.5k 0.5× 1.5k 1.1× 1.6k 1.3× 467 0.5× 59 4.1k
G.R. Wyatt Canada 43 2.6k 0.9× 2.7k 0.9× 1.8k 1.3× 2.2k 1.7× 530 0.6× 105 6.3k
Mary Bownes United Kingdom 41 2.7k 0.9× 2.7k 0.9× 2.2k 1.7× 1.4k 1.1× 819 0.9× 136 5.9k
Chantal Dauphin‐Villemant France 26 1.8k 0.6× 1.1k 0.4× 796 0.6× 1.1k 0.9× 497 0.6× 44 3.2k
Ryusuke Niwa Japan 37 2.3k 0.8× 2.0k 0.7× 1.0k 0.8× 1.3k 1.0× 583 0.7× 87 4.7k
Dick J. Van der Horst Netherlands 28 1.4k 0.5× 925 0.3× 526 0.4× 792 0.6× 400 0.4× 61 2.6k
Michael Bender United States 16 1.6k 0.5× 948 0.3× 660 0.5× 580 0.4× 439 0.5× 28 2.3k
Dean P. Smith United States 33 3.3k 1.1× 1.9k 0.7× 1.7k 1.3× 2.0k 1.5× 635 0.7× 70 5.2k

Countries citing papers authored by Peter Cherbas

Since Specialization
Citations

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

Fields of papers citing papers by Peter Cherbas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Cherbas

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Cherbas. A scholar is included among the top collaborators of Peter Cherbas 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 Peter Cherbas. Peter Cherbas 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.
Cherbas, Lucy, et al.. (2015). Tools for Targeted Genome Engineering of Established Drosophila Cell Lines. Genetics. 201(4). 1307–1318. 10 indexed citations
2.
Wen, Jiayu, Jaaved Mohammed, Diane Bortolamiol-Bécet, et al.. (2014). Diversity of miRNAs, siRNAs, and piRNAs across 25 Drosophila cell lines. Genome Research. 24(7). 1236–1250. 55 indexed citations
3.
Lee, Hangnoh, C. Joel McManus, Dong-Yeon Cho, et al.. (2014). DNA copy number evolution in Drosophila cell lines. Genome biology. 15(8). R70–R70. 76 indexed citations
4.
Smibert, Peter, Pedro Miura, Jakub Orzechowski Westholm, et al.. (2012). Global Patterns of Tissue-Specific Alternative Polyadenylation in Drosophila. Cell Reports. 1(3). 277–289. 187 indexed citations
5.
Cherbas, Lucy & Peter Cherbas. (2007). Transformation of Drosophila Cell Lines. Methods in molecular biology. 388. 317–340. 14 indexed citations
6.
Badenhorst, Paul, Hua Xiao, Lucy Cherbas, et al.. (2005). The Drosophila nucleosome remodeling factor NURF is required for Ecdysteroid signaling and metamorphosis. Genes & Development. 19(21). 2540–2545. 118 indexed citations
7.
Riddiford, Lynn M., Peter Cherbas, & James W. Truman. (2000). Ecdysone receptors and their biological actions. Vitamins and hormones. 60. 1–73. 476 indexed citations
9.
Swevers, Luc, Lucy Cherbas, Peter Cherbas, & Kostas Iatrou. (1996). Bombyx EcR (BmEcR) and Bombyx USP (BmCF1) combine to form a functional ecdysone receptor. Insect Biochemistry and Molecular Biology. 26(3). 217–221. 104 indexed citations
10.
D’Avino, Pier Paolo, Stefania Crispi, Lucy Cherbas, Peter Cherbas, & Maria Furia. (1995). The moulting hormone ecdysone is able to recognize target elements composed of direct repeats. Molecular and Cellular Endocrinology. 113(1). 1–9. 40 indexed citations
11.
Cherbas, Lucy, Robert F. Moss, & Peter Cherbas. (1994). Chapter 9 Transformation Techniques for Drosophila Cell Lines. Methods in cell biology. 44. 161–179. 62 indexed citations
12.
Andres, Andrew J. & Peter Cherbas. (1994). Tissue‐specific regulation by ecdysone: Distinct patterns of Eip28/29 expression are controlled by different ecdysone response elements. Developmental Genetics. 15(4). 320–331. 18 indexed citations
13.
Koelle, Michael R., William S. Talbot, William A. Segraves, et al.. (1991). The drosophila EcR gene encodes an ecdysone receptor, a new member of the steroid receptor superfamily. Cell. 67(1). 59–77. 802 indexed citations breakdown →
14.
Rebay, Ilaria, et al.. (1991). Specific EGF repeats of Notch mediate interactions with Delta and serrate: Implications for notch as a multifunctional receptor. Cell. 67(4). 687–699. 640 indexed citations breakdown →
15.
Cherbas, Lucy, et al.. (1991). Identification of ecdysone response elements by analysis of the Drosophila Eip28/29 gene.. Genes & Development. 5(1). 120–131. 189 indexed citations
16.
Spray, David C., et al.. (1989). Ionic coupling and mitotic synchrony of siblings in a Drosophila cell line. Experimental Cell Research. 184(2). 509–517. 9 indexed citations
17.
Cherbas, Peter, Lucy Cherbas, Shoei‐Sheng Lee, & K Nakanishi. (1988). 26-[125I]iodoponasterone A is a potent ecdysone and a sensitive radioligand for ecdysone receptors.. Proceedings of the National Academy of Sciences. 85(7). 2096–2100. 64 indexed citations
18.
Savakis, Charalambos, M. Koehler, & Peter Cherbas. (1984). cDNA clones for the ecdysone-inducible polypeptide (EIP) mRNAs of Drosophila Kc cells. The EMBO Journal. 3(1). 235–243. 42 indexed citations
19.
Beltz, Gerald A., Kenneth Jacobs, Thomas H. Eickbush, Peter Cherbas, & Fotis C. Kafatos. (1983). [19] Isolation of multigene families and determination of homologies by filter hybridization methods. Methods in enzymology on CD-ROM/Methods in enzymology. 100. 266–285. 115 indexed citations
20.
Cherbas, Lucy, et al.. (1980). The morphological response of Kc-H cells to ecdysteroids: Hormonal specificity. Development Genes and Evolution. 189(1). 1–15. 88 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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