Brandon Wadas

588 total citations
10 papers, 430 citations indexed

About

Brandon Wadas is a scholar working on Molecular Biology, Oncology and Reproductive Medicine. According to data from OpenAlex, Brandon Wadas has authored 10 papers receiving a total of 430 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 5 papers in Oncology and 4 papers in Reproductive Medicine. Recurrent topics in Brandon Wadas's work include Ubiquitin and proteasome pathways (6 papers), Peptidase Inhibition and Analysis (5 papers) and Hypothalamic control of reproductive hormones (4 papers). Brandon Wadas is often cited by papers focused on Ubiquitin and proteasome pathways (6 papers), Peptidase Inhibition and Analysis (5 papers) and Hypothalamic control of reproductive hormones (4 papers). Brandon Wadas collaborates with scholars based in United States, South Korea and Russia. Brandon Wadas's co-authors include Alexander Varshavsky, Jang‐Hyun Oh, Shun‐Jia Chen, Hanna Cho, Jeong‐Mok Kim, Seon‐Young Kim, Christopher S. Brower, Stéphanie Constantin, Christine L. Jasoni and Allan E. Herbison and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Brandon Wadas

10 papers receiving 429 citations

Peers

Brandon Wadas

MB

ONCOL

RM

GENET

EPIDE

Joshua E. Cottom United States

H Ogino Japan

Elaine Vickers Canada

Jessica L. Sneeden United States

Adam Diehl United States

Ann Polonskaia United States

Barbara J. Seeler United States

Kuo-Liang Hsi Canada

Kalliope E. Sekeri‐Pataryas Greece

Irene Meliciani Germany

Joshua E. Cottom United States

Brandon Wadas

435 ×1.3

MB

38 ×0.2

ONCOL

184 ×3.1

RM

148 ×3.0

GENET

14 ×0.5

EPIDE

Citations per year, relative to Brandon Wadas Brandon Wadas (= 1×) peers Joshua E. Cottom

Countries citing papers authored by Brandon Wadas

Since Specialization

Citations

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

Fields of papers citing papers by Brandon Wadas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brandon Wadas

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

All Works

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10 of 10 papers shown

1.

Chen, Shun‐Jia, et al.. (2017). An N-end rule pathway that recognizes proline and destroys gluconeogenic enzymes. Science. 355(6323). 157 indexed citations

2.

Wadas, Brandon, et al.. (2016). Degradation of Serotonin N-Acetyltransferase, a Circadian Regulator, by the N-end Rule Pathway. Journal of Biological Chemistry. 291(33). 17178–17196. 15 indexed citations

3.

Wadas, Brandon, Konstantin Piatkov, Christopher S. Brower, & Alexander Varshavsky. (2016). Analyzing N-terminal Arginylation through the Use of Peptide Arrays and Degradation Assays. Journal of Biological Chemistry. 291(40). 20976–20992. 23 indexed citations

4.

Liu, Yujiao, Chao Liu, ZeNan Chang, et al.. (2016). Degradation of the Separase-cleaved Rec8, a Meiotic Cohesin Subunit, by the N-end Rule Pathway. Journal of Biological Chemistry. 291(14). 7426–7438. 20 indexed citations

5.

Kim, Jeong‐Mok, et al.. (2015). Control of mammalian G protein signaling by N-terminal acetylation and the N-end rule pathway. Science. 347(6227). 1249–1252. 122 indexed citations

6.

Brower, Christopher S., Connor Rosen, Richard H. Jones, et al.. (2014). Liat1, an arginyltransferase-binding protein whose evolution among primates involved changes in the numbers of its 10-residue repeats. Proceedings of the National Academy of Sciences. 111(46). E4936–45. 17 indexed citations

7.

Wadas, Brandon, Emily R. Aurand, Charles E. Roselli, et al.. (2010). Prenatal Exposure to Vinclozolin Disrupts Selective Aspects of the Gonadotrophin‐Releasing Hormone Neuronal System of the Rabbit. Journal of Neuroendocrinology. 22(6). 518–526. 5 indexed citations

8.

Constantin, Stéphanie, Christine L. Jasoni, Brandon Wadas, & Allan E. Herbison. (2009). γ-Aminobutyric Acid and Glutamate Differentially Regulate Intracellular Calcium Concentrations in Mouse Gonadotropin-Releasing Hormone Neurons. Endocrinology. 151(1). 262–270. 40 indexed citations

9.

Gill, John C., Brandon Wadas, Peilin Chen, et al.. (2008). The Gonadotropin-Releasing Hormone (GnRH) Neuronal Population Is Normal in Size and Distribution in GnRH-Deficient and GnRH Receptor-Mutant Hypogonadal Mice. Endocrinology. 149(9). 4596–4604. 30 indexed citations

10.

Moenter, Suzanne M., Stuart Tobet, Ursula B. Kaiser, et al.. (2008). Hypogonadal Mice in Size and Distribution in GnRH-Deficient and GnRH Receptor-Mutant The Gonadotropin-Releasing Hormone (GnRH) Neuronal Population Is Normal. 1 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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