Paul A. Ney

20.6k total citations · 3 hit papers
55 papers, 6.7k citations indexed

About

Paul A. Ney is a scholar working on Molecular Biology, Epidemiology and Physiology. According to data from OpenAlex, Paul A. Ney has authored 55 papers receiving a total of 6.7k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 15 papers in Epidemiology and 11 papers in Physiology. Recurrent topics in Paul A. Ney's work include Autophagy in Disease and Therapy (15 papers), Erythrocyte Function and Pathophysiology (9 papers) and Mitochondrial Function and Pathology (8 papers). Paul A. Ney is often cited by papers focused on Autophagy in Disease and Therapy (15 papers), Erythrocyte Function and Pathophysiology (9 papers) and Mitochondrial Function and Pathology (8 papers). Paul A. Ney collaborates with scholars based in United States, Russia and Canada. Paul A. Ney's co-authors include Mingjie Zhang, Mondira Kundu, Arthur W. Nienhuis, Brian P. Sorrentino, Melanie R. Loyd, Ji Zhang, Ji Zhang, Frank C. Dorsey, John L. Cleveland and Kevin T. McDonagh and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Paul A. Ney

54 papers receiving 6.6k citations

Hit Papers

Nix is a selective autophagy receptor for mitochondrial c... 2007 2026 2013 2019 2009 2009 2007 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paul A. Ney United States 36 4.2k 3.0k 1.1k 768 576 55 6.7k
Leon O. Murphy United States 25 6.6k 1.6× 1.8k 0.6× 993 0.9× 1.3k 1.6× 423 0.7× 31 9.3k
Andrew R. Tee United Kingdom 39 6.6k 1.6× 1.3k 0.4× 1.5k 1.5× 1.3k 1.7× 335 0.6× 69 9.1k
Xianxin Hua United States 42 5.5k 1.3× 1.5k 0.5× 490 0.5× 868 1.1× 170 0.3× 102 8.8k
Carson C. Thoreen United States 25 10.3k 2.4× 1.8k 0.6× 1.1k 1.0× 1.9k 2.5× 402 0.7× 36 12.7k
Stephan Wullschleger Switzerland 21 6.3k 1.5× 1.1k 0.4× 703 0.7× 950 1.2× 345 0.6× 26 8.8k
Joseph T. Opferman United States 43 6.0k 1.4× 1.9k 0.6× 612 0.6× 1.2k 1.6× 361 0.6× 95 9.5k
Timothy R. Peterson United States 15 6.1k 1.4× 1.3k 0.4× 1.1k 1.0× 1.3k 1.7× 252 0.4× 17 8.1k
Brian Leber Canada 49 6.3k 1.5× 853 0.3× 831 0.8× 576 0.8× 1.1k 1.9× 196 9.5k
Alejo Efeyan Spain 24 6.0k 1.4× 1.7k 0.6× 1.9k 1.8× 1.3k 1.7× 252 0.4× 49 9.5k
David B. Shackelford United States 23 6.1k 1.4× 2.7k 0.9× 938 0.9× 816 1.1× 170 0.3× 36 8.7k

Countries citing papers authored by Paul A. Ney

Since Specialization
Citations

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

Fields of papers citing papers by Paul A. Ney

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul A. Ney. 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 Paul A. Ney. The network helps show where Paul A. Ney may publish in the future.

Co-authorship network of co-authors of Paul A. Ney

This figure shows the co-authorship network connecting the top 25 collaborators of Paul A. Ney. A scholar is included among the top collaborators of Paul A. Ney 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 Paul A. Ney. Paul A. Ney 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.
Turnis, Meghan E., et al.. (2021). Requirement for antiapoptotic MCL-1 during early erythropoiesis. Blood. 137(14). 1945–1958. 14 indexed citations
2.
Ney, Paul A.. (2015). Mitochondrial autophagy: Origins, significance, and role of BNIP3 and NIX. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1853(10). 2775–2783. 260 indexed citations
3.
Ney, Paul A.. (2011). Normal and disordered reticulocyte maturation. Current Opinion in Hematology. 18(3). 152–157. 99 indexed citations
4.
Joo, Joung Hyuck, Frank C. Dorsey, Aashish Joshi, et al.. (2011). Hsp90-Cdc37 Chaperone Complex Regulates Ulk1- and Atg13-Mediated Mitophagy. Molecular Cell. 43(4). 572–585. 186 indexed citations
5.
Zhang, Ji & Paul A. Ney. (2010). Reticulocyte mitophagy: Monitoring mitochondrial clearance in a mammalian model. Autophagy. 6(3). 405–408. 33 indexed citations
6.
Zhou, Zhuo, Li X, Changwang Deng, et al.. (2010). USF and NF-E2 Cooperate to Regulate the Recruitment and Activity of RNA Polymerase II in the β-Globin Gene Locus. Journal of Biological Chemistry. 285(21). 15894–15905. 28 indexed citations
7.
Zhang, Ji, Mondira Kundu, & Paul A. Ney. (2009). Chapter 15 Mitophagy in Mammalian Cells. Methods in enzymology on CD-ROM/Methods in enzymology. 452. 227–245. 21 indexed citations
8.
Zhang, Mingjie & Paul A. Ney. (2009). Role of BNIP3 and NIX in cell death, autophagy, and mitophagy. Cell Death and Differentiation. 16(7). 939–946. 800 indexed citations breakdown →
9.
Zhang, Ji & Paul A. Ney. (2008). NIX induces mitochondrial autophagy in reticulocytes. Autophagy. 4(3). 354–356. 73 indexed citations
10.
Leonis, Mike A., Arlene E. Dent, Meredith A. Olson, et al.. (2006). Short-form Ron receptor is required for normal IFN-γ production in concanavalin A-induced acute liver injury. American Journal of Physiology-Gastrointestinal and Liver Physiology. 292(1). G253–G261. 12 indexed citations
11.
Ney, Paul A.. (2006). Gene expression during terminal erythroid differentiation. Current Opinion in Hematology. 13(4). 203–208. 22 indexed citations
12.
Dominici, Massimo, Esther R. Allay, Kelli L. Boyd, et al.. (2005). Transgenic mice with pancellular enhanced green fluorescent protein expression in primitive hematopoietic cells and all blood cell progeny. genesis. 42(1). 17–22. 26 indexed citations
13.
14.
Kasper, Lawryn H., Fayçal Boussouar, Paul A. Ney, et al.. (2002). A transcription-factor-binding surface of coactivator p300 is required for haematopoiesis. Nature. 419(6908). 738–743. 154 indexed citations
15.
Li, Youjun, Rachel R. Higgins, Brian J. Pak, et al.. (2001). p45 NFE2 Is a Negative Regulator of Erythroid Proliferation Which Contributes to the Progression of Friend Virus-Induced Erythroleukemias. Molecular and Cellular Biology. 21(1). 73–80. 20 indexed citations
16.
Persons, Derek A., Robert F. Paulson, Melanie R. Loyd, et al.. (1999). Fv2 encodes a truncated form of the Stk receptor tyrosine kinase. Nature Genetics. 23(2). 159–165. 120 indexed citations
17.
Ney, Paul A., et al.. (1997). Multiple regions of p45 NF-E2 are required for  -globin gene expression in erythroid cells. Nucleic Acids Research. 25(12). 2509–2515. 40 indexed citations
18.
Ney, Paul A., Nancy C. Andrews, Stephen M. Jane, et al.. (1993). Purification of the Human NF-E2 Complex: cDNA Cloning of the Hematopoietic Cell-Specific Subunit and Evidence for an Associated Partner. Molecular and Cellular Biology. 13(9). 5604–5612. 42 indexed citations
19.
Sorrentino, Brian P., Paul A. Ney, & Arthur W. Nienhuis. (1990). Localization and Characterization of the DNase I–Hypersensitive Site II (HS II) Enhancer. Annals of the New York Academy of Sciences. 612(1). 141–151. 7 indexed citations
20.
Ney, Paul A., et al.. (1990). Inducibility of the HS II enhancer depends on binding of an erythroid specific nuclear protein. Nucleic Acids Research. 18(20). 6011–6017. 126 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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