Mark Thomas

12.2k total citations · 3 hit papers
38 papers, 4.3k citations indexed

About

Mark Thomas is a scholar working on Molecular Biology, Genetics and Physiology. According to data from OpenAlex, Mark Thomas has authored 38 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 8 papers in Genetics and 6 papers in Physiology. Recurrent topics in Mark Thomas's work include Muscle Physiology and Disorders (21 papers), CRISPR and Genetic Engineering (6 papers) and Cardiomyopathy and Myosin Studies (5 papers). Mark Thomas is often cited by papers focused on Muscle Physiology and Disorders (21 papers), CRISPR and Genetic Engineering (6 papers) and Cardiomyopathy and Myosin Studies (5 papers). Mark Thomas collaborates with scholars based in New Zealand, United Kingdom and Singapore. Mark Thomas's co-authors include Mridula Sharma, Ravi Kambadur, Brett Langley, Seumas McCroskery, Amy Bishop, Linda Maxwell, Vivek Iyer, Alex Hennebry, Jennifer Harrow and Allan Bradley and has published in prestigious journals such as Nature, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Mark Thomas

35 papers receiving 4.2k citations

Hit Papers

A conditional knockout resource for the genome-wide st... 2002 2026 2010 2018 2011 2002 2003 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Thomas New Zealand 23 3.5k 962 806 737 419 38 4.3k
Arnaud Ferry France 41 3.3k 0.9× 1.1k 1.2× 1.0k 1.3× 467 0.6× 377 0.9× 146 4.7k
Daina Z. Ewton United States 34 3.8k 1.1× 606 0.6× 850 1.1× 577 0.8× 448 1.1× 46 5.0k
Douglas P. Millay United States 30 2.9k 0.8× 657 0.7× 440 0.5× 347 0.5× 365 0.9× 53 3.4k
C. Florian Bentzinger Canada 22 4.1k 1.2× 1.2k 1.3× 659 0.8× 435 0.6× 967 2.3× 31 4.9k
Ann M. Lawler United States 20 5.3k 1.5× 1.7k 1.8× 871 1.1× 1.4k 2.0× 548 1.3× 25 7.4k
Silvia Brunelli Italy 39 3.1k 0.9× 873 0.9× 363 0.5× 716 1.0× 839 2.0× 84 4.9k
Hang Yin United States 27 4.0k 1.2× 995 1.0× 387 0.5× 423 0.6× 703 1.7× 58 5.1k
Gregory A. Cox United States 38 3.3k 1.0× 558 0.6× 497 0.6× 582 0.8× 200 0.5× 82 4.4k
Christopher J. Mann United Kingdom 24 2.2k 0.6× 510 0.5× 283 0.4× 625 0.8× 665 1.6× 38 3.3k
William Poueymirou United States 20 4.8k 1.4× 1.5k 1.6× 1.2k 1.5× 662 0.9× 286 0.7× 29 6.8k

Countries citing papers authored by Mark Thomas

Since Specialization
Citations

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

Fields of papers citing papers by Mark Thomas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Thomas

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Thomas. A scholar is included among the top collaborators of Mark Thomas 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 Mark Thomas. Mark Thomas 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.
Thomas, Mark, et al.. (2024). Advanced biomolecular spectroscopic profiling of cardiovascular disease macromolecular markers: SIL-6, IL-9, LpA, ApoB, PCSK9 and NT-ProBNP for rapid in-situ detection and monitoring. International Journal of Biological Macromolecules. 284(Pt 1). 138115–138115. 1 indexed citations
3.
Thomas, Mark, Gaétan Burgio, David J. Adams, & Vivek Iyer. (2019). Collateral damage and CRISPR genome editing. PLoS Genetics. 15(3). e1007994–e1007994. 26 indexed citations
4.
Thomas, Mark, David Parry-Smith, & Vivek Iyer. (2019). Best practice for CRISPR design using current tools and resources. Methods. 164-165. 3–17. 12 indexed citations
5.
Iyer, Vivek, Katharina Boroviak, Mark Thomas, et al.. (2018). No unexpected CRISPR-Cas9 off-target activity revealed by trio sequencing of gene-edited mice. PLoS Genetics. 14(7). e1007503–e1007503. 87 indexed citations
6.
Guan, Jian, Peter D. Gluckman, Xueying Sun, et al.. (2014). Cyclic glycine-proline regulates IGF-1 homeostasis by altering the binding of IGFBP-3 to IGF-1. Scientific Reports. 4(1). 4388–4388. 41 indexed citations
7.
Jeanplong, Ferenc, Shelley Falconer, Jenny M. Oldham, et al.. (2013). Identification and expression of a novel transcript of the growth and differentiation factor-11 gene. Molecular and Cellular Biochemistry. 390(1-2). 9–18. 5 indexed citations
8.
Jeanplong, Ferenc, Shelley Falconer, Mark Thomas, et al.. (2012). Growth and differentiation factor-11 is developmentally regulated in skeletal muscle and inhibits myoblast differentiation. 2(4). 127–138. 14 indexed citations
9.
Skarnes, William C., Barry P. Rosen, Anthony P. West, et al.. (2011). A conditional knockout resource for the genome-wide study of mouse gene function. Nature. 474(7351). 337–342. 1208 indexed citations breakdown →
10.
Salerno, Mônica Senna, et al.. (2009). Akirin1 (Mighty), a novel promyogenic factor regulates muscle regeneration and cell chemotaxis. Experimental Cell Research. 315(12). 2012–2021. 40 indexed citations
11.
Marshall, Amy D., Mônica Senna Salerno, Mark Thomas, et al.. (2008). Mighty is a novel promyogenic factor in skeletal myogenesis. Experimental Cell Research. 314(5). 1013–1029. 70 indexed citations
12.
McFarlane, Craig, Alex Hennebry, Mark Thomas, et al.. (2007). Myostatin signals through Pax7 to regulate satellite cell self-renewal. Experimental Cell Research. 314(2). 317–329. 118 indexed citations
13.
Kishioka, Yasuhiro, Mark Thomas, Jun‐ichi Wakamatsu, et al.. (2007). Decorin enhances the proliferation and differentiation of myogenic cells through suppressing myostatin activity. Journal of Cellular Physiology. 215(3). 856–867. 98 indexed citations
14.
McFarlane, Craig, Erin Plummer, Mark Thomas, et al.. (2006). Myostatin induces cachexia by activating the ubiquitin proteolytic system through an NF‐κB‐independent, FoxO1‐dependent mechanism. Journal of Cellular Physiology. 209(2). 501–514. 370 indexed citations
15.
McFarlane, Craig, Brett Langley, Mark Thomas, et al.. (2005). Proteolytic processing of myostatin is auto-regulated during myogenesis. Developmental Biology. 283(1). 58–69. 46 indexed citations
16.
Nicholas, G., Carole Berry, Trevor Watson, et al.. (2005). Myostatin negatively regulates the expression of the steroid receptor co‐factor ARA70. Journal of Cellular Physiology. 206(1). 255–263. 16 indexed citations
17.
Langley, Brett, et al.. (2004). Myostatin inhibits rhabdomyosarcoma cell proliferation through an Rb-independent pathway. Oncogene. 23(2). 524–534. 39 indexed citations
18.
Spiller, Michael P., Ravi Kambadur, Ferenc Jeanplong, et al.. (2002). The Myostatin Gene Is a Downstream Target Gene of Basic Helix-Loop-Helix Transcription Factor MyoD. Molecular and Cellular Biology. 22(20). 7066–7082. 136 indexed citations
19.
Berry, Carole, Mark Thomas, Brett Langley, Mridula Sharma, & Ravi Kambadur. (2002). Single cysteine to tyrosine transition inactivates the growth inhibitory function of Piedmontese myostatin. American Journal of Physiology-Cell Physiology. 283(1). C135–C141. 51 indexed citations
20.
Sharma, Mahesh C., Ferenc Jeanplong, Mark Thomas, et al.. (2000). Cloning and characterization of the bovine myostatin promoter. 60. 90–93. 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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