Michael T. Marr

5.2k total citations · 1 hit paper
41 papers, 3.2k citations indexed

About

Michael T. Marr is a scholar working on Molecular Biology, Genetics and Ecology. According to data from OpenAlex, Michael T. Marr has authored 41 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 9 papers in Genetics and 7 papers in Ecology. Recurrent topics in Michael T. Marr's work include RNA and protein synthesis mechanisms (15 papers), RNA Research and Splicing (11 papers) and Bacterial Genetics and Biotechnology (9 papers). Michael T. Marr is often cited by papers focused on RNA and protein synthesis mechanisms (15 papers), RNA Research and Splicing (11 papers) and Bacterial Genetics and Biotechnology (9 papers). Michael T. Marr collaborates with scholars based in United States, France and United Kingdom. Michael T. Marr's co-authors include Jeffrey W. Roberts, Robert Tjian, Óscar Puig, Joo‐Seop Park, Kevin J. Wright, Konstantin Severinov, Joseph Rodriguez, Yevgenia L. Khodor, Katharine C. Abruzzi and Michael Rosbash and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Michael T. Marr

40 papers receiving 3.1k citations

Hit Papers

Control of cell number by Drosophila FOXO: downstream and... 2003 2026 2010 2018 2003 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael T. Marr United States 25 2.6k 949 496 321 309 41 3.2k
Robin P. Wharton United States 24 3.5k 1.4× 769 0.8× 242 0.5× 334 1.0× 266 0.9× 38 4.0k
Hilary Ellis United States 11 2.3k 0.9× 1.4k 1.5× 502 1.0× 201 0.6× 112 0.4× 14 2.9k
Gloria A. Brar United States 23 6.1k 2.4× 770 0.8× 153 0.3× 141 0.4× 263 0.9× 40 6.6k
Kenneth C. Burtis United States 22 1.7k 0.7× 1.3k 1.4× 160 0.3× 854 2.7× 98 0.3× 29 2.8k
Elmar Wahle Germany 48 7.0k 2.7× 732 0.8× 193 0.4× 462 1.4× 72 0.2× 88 7.6k
Amy C. Groth United States 10 2.0k 0.8× 835 0.9× 270 0.5× 391 1.2× 58 0.2× 12 2.4k
Craig A. Smibert Canada 32 2.5k 1.0× 470 0.5× 76 0.2× 111 0.3× 134 0.4× 45 3.1k
Asako Sugimoto Japan 30 2.6k 1.0× 522 0.6× 187 0.4× 167 0.5× 1.1k 3.5× 72 3.9k

Countries citing papers authored by Michael T. Marr

Since Specialization
Citations

This map shows the geographic impact of Michael T. Marr'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 Michael T. Marr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael T. Marr more than expected).

Fields of papers citing papers by Michael T. Marr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael T. Marr. 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 Michael T. Marr. The network helps show where Michael T. Marr may publish in the future.

Co-authorship network of co-authors of Michael T. Marr

This figure shows the co-authorship network connecting the top 25 collaborators of Michael T. Marr. A scholar is included among the top collaborators of Michael T. Marr 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 Michael T. Marr. Michael T. Marr 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
2.
Zeng, Baosheng, et al.. (2022). The small RNA landscape is stable with age and resistant to loss of dFOXO signaling in Drosophila. PLoS ONE. 17(11). e0273590–e0273590. 1 indexed citations
3.
Levandowski, Cecilia B., Kapil Gupta, Jonathan D. Rubin, et al.. (2020). TFIID Enables RNA Polymerase II Promoter-Proximal Pausing. Molecular Cell. 78(4). 785–793.e8. 54 indexed citations
4.
Royer, Leandro, et al.. (2018). The Ras-like GTPase Rem2 is a potent inhibitor of calcium/calmodulin-dependent kinase II activity. Journal of Biological Chemistry. 293(38). 14798–14811. 10 indexed citations
5.
6.
Kang, Min‐Ji, Deepika Vasudevan, Kwonyoon Kang, et al.. (2016). 4E-BP is a target of the GCN2–ATF4 pathway during Drosophila development and aging. The Journal of Cell Biology. 216(1). 115–129. 78 indexed citations
7.
Marr, Michael T., et al.. (2016). dFOXO Activates Large and Small Heat Shock Protein Genes in Response to Oxidative Stress to Maintain Proteostasis in Drosophila. Journal of Biological Chemistry. 291(36). 19042–19050. 32 indexed citations
8.
Marr, Sharon K., John T. Lis, Jessica E. Treisman, & Michael T. Marr. (2014). The Metazoan-Specific Mediator Subunit 26 (Med26) Is Essential for Viability and Is Found at both Active Genes and Pericentric Heterochromatin in Drosophila melanogaster. Molecular and Cellular Biology. 34(14). 2710–2720. 16 indexed citations
9.
Ghiretti, Amy E., et al.. (2013). CaMKII-Dependent Phosphorylation of the GTPase Rem2 Is Required to Restrict Dendritic Complexity. Journal of Neuroscience. 33(15). 6504–6515. 24 indexed citations
10.
Marr, Sharon K., Katie L. Pennington, & Michael T. Marr. (2012). Efficient metal-specific transcription activation by Drosophila MTF-1 requires conserved cysteine residues in the carboxy-terminal domain. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 1819(8). 902–912. 14 indexed citations
11.
Khodor, Yevgenia L., Joseph Rodriguez, Katharine C. Abruzzi, et al.. (2011). Nascent-seq indicates widespread cotranscriptional pre-mRNA splicing in Drosophila. Genes & Development. 25(23). 2502–2512. 194 indexed citations
12.
Kadener, Sebastián, Joseph Rodriguez, Katharine C. Abruzzi, et al.. (2009). Genome-wide identification of targets of the drosha–pasha/DGCR8 complex. RNA. 15(4). 537–545. 94 indexed citations
13.
Marr, Michael T., et al.. (2008). MyoD Targets TAF3/TRF3 to Activate Myogenin Transcription. Molecular Cell. 32(1). 96–105. 123 indexed citations
14.
Casas‐Tintó, Sergio, et al.. (2007). Characterization of the Drosophila insulin receptor promoter. Biochimica et Biophysica Acta (BBA) - Gene Structure and Expression. 1769(4). 236–243. 16 indexed citations
15.
Marr, Michael T., Yoh Isogai, Kevin J. Wright, & Robert Tjian. (2006). Coactivator cross-talk specifies transcriptional output. Genes & Development. 20(11). 1458–1469. 76 indexed citations
16.
Adelman, Karen, Michael T. Marr, Janis Werner, et al.. (2005). Efficient Release from Promoter-Proximal Stall Sites Requires Transcript Cleavage Factor TFIIS. Molecular Cell. 17(1). 103–112. 142 indexed citations
17.
Taatjes, Dylan J., Michael T. Marr, & Robert Tjian. (2004). Regulatory diversity among metazoan co-activator complexes. Nature Reviews Molecular Cell Biology. 5(5). 403–410. 114 indexed citations
18.
Marr, Michael T. & Jeffrey W. Roberts. (2000). Function of Transcription Cleavage Factors GreA and GreB at a Regulatory Pause Site. Molecular Cell. 6(6). 1275–1285. 123 indexed citations
19.
Chan, Clement L. K., Changrui Lu, Michael T. Marr, et al.. (1999). The interface of sigma with core RNA polymerase is extensive, conserved, and functionally specialized. Genes & Development. 13(22). 3015–3026. 358 indexed citations
20.
Severinova, Elena, Konstantin Severinov, David Fenyö, et al.. (1996). Domain Organization of the RNA Polymerase σ70Subunit. Journal of Molecular Biology. 263(5). 637–647. 124 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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