Michael C. Dyle

1.4k total citations
17 papers, 1.0k citations indexed

About

Michael C. Dyle is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Genetics. According to data from OpenAlex, Michael C. Dyle has authored 17 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 4 papers in Cellular and Molecular Neuroscience and 3 papers in Genetics. Recurrent topics in Michael C. Dyle's work include Muscle Physiology and Disorders (10 papers), Genetic Neurodegenerative Diseases (4 papers) and Cardiomyopathy and Myosin Studies (3 papers). Michael C. Dyle is often cited by papers focused on Muscle Physiology and Disorders (10 papers), Genetic Neurodegenerative Diseases (4 papers) and Cardiomyopathy and Myosin Studies (3 papers). Michael C. Dyle collaborates with scholars based in United States, Italy and Russia. Michael C. Dyle's co-authors include Christopher M. Adams, Scott M. Ebert, Steven A. Bullard, Kale S. Bongers, Daniel K. Fox, Steven D. Kunkel, Jason M. Dierdorff, Christopher Elmore, Sujatha Jagannathan and Michael A. Cortázar and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and The FASEB Journal.

In The Last Decade

Michael C. Dyle

17 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
Michael C. Dyle United States 15 762 314 184 106 104 17 1.0k
Steven D. Kunkel United States 7 707 0.9× 316 1.0× 168 0.9× 94 0.9× 111 1.1× 8 963
Kevin A. Voelker United States 13 604 0.8× 295 0.9× 161 0.9× 88 0.8× 161 1.5× 16 920
Jason M. Dierdorff United States 11 570 0.7× 279 0.9× 170 0.9× 100 0.9× 79 0.8× 13 760
Steven A. Bullard United States 21 1.1k 1.4× 315 1.0× 236 1.3× 115 1.1× 96 0.9× 23 1.4k
Alexandre Prola France 18 885 1.2× 267 0.9× 261 1.4× 256 2.4× 67 0.6× 31 1.5k
Eili Tranheim Kase Norway 22 661 0.9× 581 1.9× 259 1.4× 126 1.2× 117 1.1× 51 1.3k
Tatsuyoshi Kono United States 19 521 0.7× 245 0.8× 207 1.1× 134 1.3× 44 0.4× 39 1.2k
Henning F. Kramer United States 13 977 1.3× 563 1.8× 253 1.4× 130 1.2× 159 1.5× 15 1.3k
Seyeon Oh South Korea 19 327 0.4× 167 0.5× 98 0.5× 101 1.0× 58 0.6× 71 954
Dorit Avni Israel 18 1.0k 1.4× 218 0.7× 237 1.3× 108 1.0× 25 0.2× 28 1.6k

Countries citing papers authored by Michael C. Dyle

Since Specialization
Citations

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

Fields of papers citing papers by Michael C. Dyle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael C. Dyle

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

All Works

17 of 17 papers shown
1.
Campbell, Amy E., Michael C. Dyle, Tyler Matheny, et al.. (2023). Compromised nonsense-mediated RNA decay results in truncated RNA-binding protein production upon DUX4 expression. Cell Reports. 42(6). 112642–112642. 16 indexed citations
2.
Zaslav, Kenneth R., Jack Farr, Michael C. Dyle, et al.. (2021). Treatment of post‐meniscectomy knee symptoms with medial meniscus replacement results in greater pain reduction and functional improvement than non‐surgical care. Knee Surgery Sports Traumatology Arthroscopy. 30(4). 1325–1335. 18 indexed citations
3.
Andersen, Nicholas D., et al.. (2019). Accelerated Marfan syndrome model recapitulates established signaling pathways. Journal of Thoracic and Cardiovascular Surgery. 159(5). 1719–1726. 4 indexed citations
4.
Dvoretskiy, Svyatoslav, Jiayu Leong, Michael C. Dyle, et al.. (2019). Pericyte transplantation improves skeletal muscle recovery following hindlimb immobilization. The FASEB Journal. 33(6). 7694–7706. 24 indexed citations
5.
Dyle, Michael C., et al.. (2019). How to get away with nonsense: Mechanisms and consequences of escape from nonsense‐mediated RNA decay. Wiley Interdisciplinary Reviews - RNA. 11(1). e1560–e1560. 71 indexed citations
6.
Adams, Christopher M., Scott M. Ebert, & Michael C. Dyle. (2017). Role of ATF4 in skeletal muscle atrophy. Current Opinion in Clinical Nutrition & Metabolic Care. 20(3). 164–168. 33 indexed citations
7.
Bullard, Steven A., Seongjin Seo, Birgit Schilling, et al.. (2016). Gadd45a Protein Promotes Skeletal Muscle Atrophy by Forming a Complex with the Protein Kinase MEKK4. Journal of Biological Chemistry. 291(34). 17496–17509. 40 indexed citations
8.
Ebert, Scott M., Michael C. Dyle, Steven A. Bullard, et al.. (2015). Identification and Small Molecule Inhibition of an Activating Transcription Factor 4 (ATF4)-dependent Pathway to Age-related Skeletal Muscle Weakness and Atrophy. Journal of Biological Chemistry. 290(42). 25497–25511. 90 indexed citations
9.
Adams, Christopher M., Scott M. Ebert, & Michael C. Dyle. (2015). Use of mRNA expression signatures to discover small molecule inhibitors of skeletal muscle atrophy. Current Opinion in Clinical Nutrition & Metabolic Care. 18(3). 263–268. 17 indexed citations
10.
Dyle, Michael C., Scott M. Ebert, Daniel P. Cook, et al.. (2014). Systems-based Discovery of Tomatidine as a Natural Small Molecule Inhibitor of Skeletal Muscle Atrophy. Journal of Biological Chemistry. 289(21). 14913–14924. 105 indexed citations
11.
Fox, Daniel K., Scott M. Ebert, Kale S. Bongers, et al.. (2014). p53 and ATF4 mediate distinct and additive pathways to skeletal muscle atrophy during limb immobilization. American Journal of Physiology-Endocrinology and Metabolism. 307(3). E245–E261. 91 indexed citations
12.
Dyle, Michael C.. (2014). Test english as a foreign language. 1 indexed citations
13.
Bongers, Kale S., Daniel K. Fox, Steven D. Kunkel, et al.. (2014). Spermine oxidase maintains basal skeletal muscle gene expression and fiber size and is strongly repressed by conditions that cause skeletal muscle atrophy. American Journal of Physiology-Endocrinology and Metabolism. 308(2). E144–E158. 47 indexed citations
14.
Bongers, Kale S., Daniel K. Fox, Scott M. Ebert, et al.. (2013). Skeletal muscle denervation causes skeletal muscle atrophy through a pathway that involves both Gadd45a and HDAC4. American Journal of Physiology-Endocrinology and Metabolism. 305(7). E907–E915. 112 indexed citations
15.
Ebert, Scott M., Michael C. Dyle, Steven D. Kunkel, et al.. (2012). Stress-induced Skeletal Muscle Gadd45a Expression Reprograms Myonuclei and Causes Muscle Atrophy. Journal of Biological Chemistry. 287(33). 27290–27301. 166 indexed citations
16.
Kunkel, Steven D., Christopher Elmore, Kale S. Bongers, et al.. (2012). Ursolic Acid Increases Skeletal Muscle and Brown Fat and Decreases Diet-Induced Obesity, Glucose Intolerance and Fatty Liver Disease. PLoS ONE. 7(6). e39332–e39332. 171 indexed citations
17.
Dyle, Michael C., et al.. (2012). Changes at the 3′-untranslated region stabilize Rubisco activase transcript levels during heat stress in Arabidopsis. Planta. 236(2). 463–476. 23 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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