Thurman M. Wheeler

2.8k total citations
19 papers, 2.2k citations indexed

About

Thurman M. Wheeler is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Thurman M. Wheeler has authored 19 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 17 papers in Cellular and Molecular Neuroscience and 3 papers in Neurology. Recurrent topics in Thurman M. Wheeler's work include Genetic Neurodegenerative Diseases (17 papers), Muscle Physiology and Disorders (13 papers) and Mitochondrial Function and Pathology (8 papers). Thurman M. Wheeler is often cited by papers focused on Genetic Neurodegenerative Diseases (17 papers), Muscle Physiology and Disorders (13 papers) and Mitochondrial Function and Pathology (8 papers). Thurman M. Wheeler collaborates with scholars based in United States, France and Poland. Thurman M. Wheeler's co-authors include Charles A. Thornton, Maurice S. Swanson, Robert T. Dirksen, John D. Lueck, Krzysztof Sobczak, C. Frank Bennett, Sanjay K. Pandey, A. Robert MacLeod, Robert J. Osborne and Masayuki Nakamori and has published in prestigious journals such as Nature, Science and New England Journal of Medicine.

In The Last Decade

Thurman M. Wheeler

19 papers receiving 2.1k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Thurman M. Wheeler 2.0k 1.3k 300 282 267 19 2.2k
Geneviève Gourdon 2.5k 1.2× 1.7k 1.3× 223 0.7× 340 1.2× 236 0.9× 79 2.8k
Marzena Wojciechowska 983 0.5× 540 0.4× 55 0.2× 126 0.4× 114 0.4× 28 1.1k
Adrian M. Timmers 1.7k 0.8× 702 0.5× 108 0.4× 81 0.3× 57 0.2× 48 1.9k
Anna Vihola 2.1k 1.0× 654 0.5× 1.3k 4.4× 154 0.5× 388 1.5× 51 2.5k
Nobutada Tachi 789 0.4× 619 0.5× 56 0.2× 234 0.8× 58 0.2× 86 1.3k
Rita Barresi 1.8k 0.9× 451 0.3× 542 1.8× 40 0.1× 239 0.9× 36 2.1k
Curtis M. Chan 990 0.5× 219 0.2× 108 0.4× 121 0.4× 353 1.3× 10 1.5k
Marco Savarese 983 0.5× 224 0.2× 477 1.6× 36 0.1× 271 1.0× 72 1.3k
Belinda S. Cowling 1.3k 0.6× 383 0.3× 481 1.6× 51 0.2× 239 0.9× 48 1.8k

Countries citing papers authored by Thurman M. Wheeler

Since Specialization
Citations

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

Fields of papers citing papers by Thurman M. Wheeler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thurman M. Wheeler. 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 Thurman M. Wheeler. The network helps show where Thurman M. Wheeler may publish in the future.

Co-authorship network of co-authors of Thurman M. Wheeler

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

All Works

19 of 19 papers shown
1.
Wheeler, Thurman M., et al.. (2023). Correction of Clcn1 alternative splicing reverses muscle fiber type transition in mice with myotonic dystrophy. Nature Communications. 14(1). 1956–1956. 5 indexed citations
2.
Kim, Eun Joo, et al.. (2020). Antisense oligonucleotide and adjuvant exercise therapy reverse fatigue in old mice with myotonic dystrophy. Molecular Therapy — Nucleic Acids. 23. 393–405. 18 indexed citations
3.
Balaj, Leonora, Sudeshna Das, Basil T. Darras, et al.. (2018). Analysis of extracellular mRNA in human urine reveals splice variant biomarkers of muscular dystrophies. Nature Communications. 9(1). 3906–3906. 36 indexed citations
4.
Baran, Timothy M., Soumya Mitra, C. Frank Bennett, et al.. (2018). Non-invasive monitoring of alternative splicing outcomes to identify candidate therapies for myotonic dystrophy type 1. Nature Communications. 9(1). 5227–5227. 20 indexed citations
5.
Jauvin, Dominic, Sanjay K. Pandey, Guillaume Bassez, et al.. (2017). Targeting DMPK with Antisense Oligonucleotide Improves Muscle Strength in Myotonic Dystrophy Type 1 Mice. Molecular Therapy — Nucleic Acids. 7. 465–474. 63 indexed citations
6.
Pandey, Sanjay K., Thurman M. Wheeler, Aneeza Kim, et al.. (2015). Identification and Characterization of Modified Antisense Oligonucleotides Targeting DMPK in Mice and Nonhuman Primates for the Treatment of Myotonic Dystrophy Type 1. Journal of Pharmacology and Experimental Therapeutics. 355(2). 329–340. 93 indexed citations
7.
Wheeler, Thurman M., et al.. (2015). Case 30-2015. New England Journal of Medicine. 373(13). 1251–1261. 2 indexed citations
8.
Nakamori, Masayuki, Krzysztof Sobczak, Araya Puwanant, et al.. (2013). Splicing biomarkers of disease severity in myotonic dystrophy. Annals of Neurology. 74(6). 862–872. 188 indexed citations
9.
Sobczak, Krzysztof, Thurman M. Wheeler, Wenli Wang, & Charles A. Thornton. (2012). RNA Interference Targeting CUG Repeats in a Mouse Model of Myotonic Dystrophy. Molecular Therapy. 21(2). 380–387. 72 indexed citations
10.
Wheeler, Thurman M., Andrew J. Leger, Sanjay K. Pandey, et al.. (2012). Targeting nuclear RNA for in vivo correction of myotonic dystrophy. Nature. 488(7409). 111–115. 383 indexed citations
11.
Mankodi, Ami, et al.. (2011). Progressive myopathy in an inducible mouse model of oculopharyngeal muscular dystrophy. Neurobiology of Disease. 45(1). 539–546. 17 indexed citations
12.
Wheeler, Thurman M., Krzysztof Sobczak, John D. Lueck, et al.. (2009). Reversal of RNA Dominance by Displacement of Protein Sequestered on Triplet Repeat RNA. Science. 325(5938). 336–339. 303 indexed citations
13.
Mulders, Susan, Walther J. A. A. van den Broek, Thurman M. Wheeler, et al.. (2009). Triplet-repeat oligonucleotide-mediated reversal of RNA toxicity in myotonic dystrophy. Proceedings of the National Academy of Sciences. 106(33). 13915–13920. 204 indexed citations
14.
Wheeler, Thurman M.. (2008). Myotonic Dystrophy: Therapeutic Strategies for the Future. Neurotherapeutics. 5(4). 592–600. 36 indexed citations
15.
Wheeler, Thurman M., et al.. (2007). Ribonuclear foci at the neuromuscular junction in myotonic dystrophy type 1. Neuromuscular Disorders. 17(3). 242–247. 44 indexed citations
16.
Wheeler, Thurman M. & Charles A. Thornton. (2007). Myotonic dystrophy: RNA-mediated muscle disease. Current Opinion in Neurology. 20(5). 572–576. 133 indexed citations
17.
Wheeler, Thurman M., John D. Lueck, Maurice S. Swanson, Robert T. Dirksen, & Charles A. Thornton. (2007). Correction of ClC-1 splicing eliminates chloride channelopathy and myotonia in mouse models of myotonic dystrophy. Journal of Clinical Investigation. 117(12). 3952–7. 195 indexed citations
18.
Kanadia, Rahul, Jihae Shin, Yuan Yuan, et al.. (2006). Reversal of RNA missplicing and myotonia after muscleblind overexpression in a mouse poly(CUG) model for myotonic dystrophy. Proceedings of the National Academy of Sciences. 103(31). 11748–11753. 274 indexed citations
19.
Bertoni, Carmen, Thurman M. Wheeler, Yining Li, et al.. (2005). Enhancement of plasmid-mediated gene therapy for muscular dystrophy by directed plasmid integration. Proceedings of the National Academy of Sciences. 103(2). 419–424. 75 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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