Megan N. McClean

1.5k total citations
34 papers, 1.0k citations indexed

About

Megan N. McClean is a scholar working on Molecular Biology, Plant Science and Cellular and Molecular Neuroscience. According to data from OpenAlex, Megan N. McClean has authored 34 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 13 papers in Plant Science and 8 papers in Cellular and Molecular Neuroscience. Recurrent topics in Megan N. McClean's work include Gene Regulatory Network Analysis (13 papers), Fungal and yeast genetics research (12 papers) and Photoreceptor and optogenetics research (8 papers). Megan N. McClean is often cited by papers focused on Gene Regulatory Network Analysis (13 papers), Fungal and yeast genetics research (12 papers) and Photoreceptor and optogenetics research (8 papers). Megan N. McClean collaborates with scholars based in United States, France and Singapore. Megan N. McClean's co-authors include Sharad Ramanathan, Pascal Hersen, L. Mahadevan, James R. Broach, Agnès Miermont, François Waharte, Sébastien Léon, Samuel Bottani, Shiqiong Hu and Ping Xu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Genetics and SHILAP Revista de lepidopterología.

In The Last Decade

Megan N. McClean

33 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Megan N. McClean United States 12 805 197 180 115 109 34 1.0k
R. Scott McIsaac United States 19 793 1.0× 99 0.5× 71 0.4× 117 1.0× 174 1.6× 27 1.1k
Jessica M. Walter United States 11 669 0.8× 127 0.6× 68 0.4× 66 0.6× 150 1.4× 14 947
Hugo Fraga Portugal 17 946 1.2× 187 0.9× 85 0.5× 99 0.9× 294 2.7× 34 1.1k
Jeremy Metz United Kingdom 20 751 0.9× 170 0.9× 106 0.6× 348 3.0× 49 0.4× 32 1.3k
Jesse T. Myers United States 8 799 1.0× 111 0.6× 86 0.5× 179 1.6× 70 0.6× 9 1.3k
Toru Ide Japan 18 1.3k 1.6× 329 1.7× 147 0.8× 168 1.5× 185 1.7× 65 1.6k
Satoru Nogami Japan 20 924 1.1× 85 0.4× 181 1.0× 241 2.1× 31 0.3× 54 1.3k
Jiřı́ Hašek Czechia 22 1.1k 1.4× 65 0.3× 327 1.8× 316 2.7× 109 1.0× 65 1.4k
Daphne H. E. W. Huberts Netherlands 9 618 0.8× 105 0.5× 63 0.3× 74 0.6× 31 0.3× 15 815
Elena Kuzmin Canada 12 595 0.7× 99 0.5× 124 0.7× 138 1.2× 41 0.4× 21 855

Countries citing papers authored by Megan N. McClean

Since Specialization
Citations

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

Fields of papers citing papers by Megan N. McClean

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Megan N. McClean. 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 Megan N. McClean. The network helps show where Megan N. McClean may publish in the future.

Co-authorship network of co-authors of Megan N. McClean

This figure shows the co-authorship network connecting the top 25 collaborators of Megan N. McClean. A scholar is included among the top collaborators of Megan N. McClean 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 Megan N. McClean. Megan N. McClean 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.
Sundling, Kaitlin E., et al.. (2024). Live-cell analysis of IMPDH protein levels during yeast colony growth provides insights into the regulation of GTP synthesis. mBio. 15(8). e0102124–e0102124. 1 indexed citations
2.
McClean, Megan N., et al.. (2024). The Yeast Optogenetic Toolkit (yOTK) for Spatiotemporal Control of Gene Expression in Budding Yeast. Methods in molecular biology. 2840. 19–36.
3.
Thompson, Jaron, et al.. (2024). Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro. ACS Synthetic Biology. 13(5). 1424–1433. 4 indexed citations
4.
McClean, Megan N., et al.. (2023). Transcription factor localization dynamics and DNA binding drive distinct promoter interpretations. Cell Reports. 42(5). 112426–112426. 6 indexed citations
6.
McClean, Megan N., et al.. (2020). Automated Calibration of Optoplate LEDs to Reduce Light Dose Variation in Optogenetic Experiments. BioTechniques. 69(4). 313–316. 6 indexed citations
7.
Ventura, Barbara Di, et al.. (2019). Optogenetic Repressors of Gene Expression in Yeasts Using Light-Controlled Nuclear Localization. Cellular and Molecular Bioengineering. 12(5). 511–528. 15 indexed citations
8.
Gasch, Audrey P., Feiqiao Brian Yu, James Hose, et al.. (2017). Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLoS Biology. 15(12). e2004050–e2004050. 107 indexed citations
9.
McClean, Megan N., et al.. (2017). Engineered bacteria self-organize to sense pressure. Nature Biotechnology. 35(11). 1045–1047. 5 indexed citations
10.
McClean, Megan N., et al.. (2017). Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications. Journal of Visualized Experiments. 3 indexed citations
11.
Dexter, Joseph P., Ping Xu, Jeremy Gunawardena, & Megan N. McClean. (2015). Robust network structure of the Sln1-Ypd1-Ssk1 three-component phospho-relay prevents unintended activation of the HOG MAPK pathway in Saccharomyces cerevisiae. BMC Systems Biology. 9(1). 17–17. 11 indexed citations
12.
Oakes, Benjamin L., et al.. (2014). Real-time optogenetic control of intracellular protein concentration in microbial cell cultures. Integrative Biology. 6(3). 366–366. 59 indexed citations
13.
Bisaria, Anjali, Pascal Hersen, & Megan N. McClean. (2014). Microfluidic Platforms for Generating Dynamic Environmental Perturbations to Study the Responses of Single Yeast Cells. Methods in molecular biology. 1205. 111–129. 2 indexed citations
14.
McIsaac, R. Scott, Sanford J. Silverman, Lance Parsons, et al.. (2013). Visualization and Analysis of mRNA Molecules Using Fluorescence <em>In Situ</em> Hybridization in <em>Saccharomyces cerevisiae</em>. Journal of Visualized Experiments. e50382–e50382. 17 indexed citations
15.
Chereji, Răzvan V., et al.. (2013). Noise and interlocking signaling pathways promote distinct transcription factor dynamics in response to different stresses. Molecular Biology of the Cell. 24(12). 2045–2057. 52 indexed citations
16.
Miermont, Agnès, François Waharte, Shiqiong Hu, et al.. (2013). Severe osmotic compression triggers a slowdown of intracellular signaling, which can be explained by molecular crowding. Proceedings of the National Academy of Sciences. 110(14). 5725–5730. 152 indexed citations
17.
McClean, Megan N., Pascal Hersen, & Sharad Ramanathan. (2011). Measuring In Vivo Signaling Kinetics in a Mitogen-Activated Kinase Pathway Using Dynamic Input Stimulation. Methods in molecular biology. 734. 101–119. 6 indexed citations
18.
McClean, Megan N., Pascal Hersen, & Sharad Ramanathan. (2009). In vivo measurement of signaling cascade dynamics. Cell Cycle. 8(3). 373–376. 7 indexed citations
19.
Hersen, Pascal, Megan N. McClean, L. Mahadevan, & Sharad Ramanathan. (2008). Signal processing by the HOG MAP kinase pathway. Proceedings of the National Academy of Sciences. 105(20). 7165–7170. 203 indexed citations
20.
McClean, Megan N., et al.. (2007). Cross-talk and decision making in MAP kinase pathways. Nature Genetics. 39(3). 409–414. 117 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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