Ann Becker

1.7k total citations · 1 hit paper
18 papers, 1.2k citations indexed

About

Ann Becker is a scholar working on Molecular Biology, Physiology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Ann Becker has authored 18 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Physiology and 5 papers in Cellular and Molecular Neuroscience. Recurrent topics in Ann Becker's work include Alzheimer's disease research and treatments (7 papers), Down syndrome and intellectual disability research (4 papers) and Cellular transport and secretion (3 papers). Ann Becker is often cited by papers focused on Alzheimer's disease research and treatments (7 papers), Down syndrome and intellectual disability research (4 papers) and Cellular transport and secretion (3 papers). Ann Becker collaborates with scholars based in United States, Germany and Japan. Ann Becker's co-authors include Charles S. Zuker, Robert W. Hardy, Yumei Sun, Michael Socolich, Susan Tsunoda, Jimena Sierralta, Emiko Suzuki, Andrew C. Zelhof, Kristin Scott and David M. Cowan and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Neuron.

In The Last Decade

Ann Becker

17 papers receiving 1.2k citations

Hit Papers

A multivalent PDZ-domain protein assembles signalling com... 1997 2026 2006 2016 1997 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A multivalent PDZ-domain protein assembles signalling complexes in a G-protein-coupled cascade
    1997 545 citations Susan Tsunoda, Jimena Sierralta et al. Nature profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ann Becker United States 10 756 604 260 117 114 18 1.2k
Ricardo Delgado Chile 22 512 0.7× 688 1.1× 259 1.0× 198 1.7× 74 0.6× 46 1.2k
Susan Tsunoda United States 17 1.3k 1.7× 1.1k 1.8× 257 1.0× 240 2.1× 131 1.1× 28 1.9k
Jairaj Acharya United States 21 1.2k 1.6× 323 0.5× 545 2.1× 87 0.7× 71 0.6× 34 1.6k
Jimena Sierralta Chile 21 991 1.3× 708 1.2× 472 1.8× 125 1.1× 102 0.9× 46 1.6k
Akihiko Tanimura Japan 21 982 1.3× 295 0.5× 276 1.1× 221 1.9× 53 0.5× 91 1.4k
Alexandra Alves‐Rodrigues Netherlands 7 796 1.1× 341 0.6× 141 0.5× 61 0.5× 47 0.4× 7 1.4k
T R Cheek United Kingdom 19 1.0k 1.4× 730 1.2× 313 1.2× 149 1.3× 33 0.3× 25 1.5k
Christian Erxleben United States 23 1.1k 1.5× 806 1.3× 172 0.7× 469 4.0× 88 0.8× 32 1.7k
Michel Villaz France 27 1.2k 1.6× 581 1.0× 157 0.6× 241 2.1× 67 0.6× 43 1.8k
Kuchuan Chen United States 10 668 0.9× 417 0.7× 250 1.0× 28 0.2× 65 0.6× 10 1.1k

Countries citing papers authored by Ann Becker

Since Specialization
Citations

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

Fields of papers citing papers by Ann Becker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ann Becker

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

All Works

18 of 18 papers shown
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Chen, Xu‐Qiao, Ann Becker, Ricardo Albay, et al.. (2024). γ‐Secretase Modulator BPN15606 Reduced Aβ42 and Aβ40 and Countered Alzheimer‐Related Pathologies in a Mouse Model of Down Syndrome. Annals of Neurology. 96(2). 390–404. 5 indexed citations
4.
Chen, Xu‐Qiao, Mariko Sawa, Ann Becker, et al.. (2023). Retromer Proteins Reduced in Down Syndrome and the Dp16 Model: Impact ofAPPDose and Preclinical Studies of a γ‐Secretase Modulator. Annals of Neurology. 94(2). 245–258. 6 indexed citations
5.
Overk, Cassia, Emma Fiorini, Marija Vukicevic, et al.. (2023). Modeling Alzheimer’s disease related phenotypes in the Ts65Dn mouse: impact of age on Aβ, Tau, pTau, NfL, and behavior. Frontiers in Neuroscience. 17. 1202208–1202208. 2 indexed citations
6.
Zoltowska, Katarzyna Marta, Sam Lismont, Thomas Enzlein, et al.. (2023). Alzheimer’s disease linked Aβ42 exerts product feedback inhibition on γ-secretase impairing downstream cell signaling. eLife. 12. 5 indexed citations
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Sawa, Mariko, Cassia Overk, Ann Becker, et al.. (2021). Impact of increased APP gene dose in Down syndrome and the Dp16 mouse model. Alzheimer s & Dementia. 18(6). 1203–1234. 26 indexed citations
9.
Wagner, Steven L., Kevin D. Rynearson, Steven K. Duddy, et al.. (2017). Pharmacological and Toxicological Properties of the Potent Oral γ-Secretase Modulator BPN-15606. Journal of Pharmacology and Experimental Therapeutics. 362(1). 31–44. 35 indexed citations
10.
Belichenko, Pavel V., et al.. (2015). Down Syndrome Cognitive Phenotypes Modeled in Mice Trisomic for All HSA 21 Homologues. PLoS ONE. 10(7). e0134861–e0134861. 50 indexed citations
11.
Zelhof, Andrew C., Robert W. Hardy, Ann Becker, & Charles S. Zuker. (2006). Transforming the architecture of compound eyes. Nature. 443(7112). 696–699. 128 indexed citations
12.
Koundakjian, Edmund J., David M. Cowan, Robert W. Hardy, & Ann Becker. (2004). The Zuker Collection: A Resource for the Analysis of Autosomal Gene Function in Drosophila melanogaster. Genetics. 167(1). 203–206. 115 indexed citations
13.
Gibbs, Sarah, Ann Becker, Robert W. Hardy, & James W. Truman. (2001). Soluble Guanylate Cyclase Is Required during Development for Visual System Function inDrosophila. Journal of Neuroscience. 21(19). 7705–7714. 42 indexed citations
14.
Tsunoda, Susan, Jimena Sierralta, Yumei Sun, et al.. (1997). A multivalent PDZ-domain protein assembles signalling complexes in a G-protein-coupled cascade. Nature. 388(6639). 243–249. 545 indexed citations breakdown →
15.
Scott, Kristin, Ann Becker, Yumei Sun, Robert W. Hardy, & Charles S. Zuker. (1995). Gqα protein function in vivo: Genetic dissection of its role in photoreceptor cell physiology. Neuron. 15(4). 919–927. 148 indexed citations
16.
Cassill, J.Aaron, et al.. (1991). Isolation of Drosophila genes encoding G protein-coupled receptor kinases.. Proceedings of the National Academy of Sciences. 88(24). 11067–11070. 78 indexed citations
17.
Hübinger, A, Ann Becker, & F. A. Gries. (1988). Total insulin levels in type 1 diabetic patients with insulin antibodies and their effect on insulin requirement and metabolic control.. PubMed. 7(2). 65–9. 7 indexed citations
18.
Becker, Ann, et al.. (1981). [Neutralizing capacity, pepsin inactivation and binding to bile acids and lysolecithin of the antacid magaldrate (author's transl)].. PubMed. 31(3). 504–7. 2 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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