Guey‐Shin Wang

2.3k total citations · 1 hit paper
17 papers, 1.8k citations indexed

About

Guey‐Shin Wang is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Guey‐Shin Wang has authored 17 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 9 papers in Cellular and Molecular Neuroscience and 4 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Guey‐Shin Wang's work include Genetic Neurodegenerative Diseases (8 papers), RNA Research and Splicing (6 papers) and Mitochondrial Function and Pathology (4 papers). Guey‐Shin Wang is often cited by papers focused on Genetic Neurodegenerative Diseases (8 papers), RNA Research and Splicing (6 papers) and Mitochondrial Function and Pathology (4 papers). Guey‐Shin Wang collaborates with scholars based in Taiwan, United States and Russia. Guey‐Shin Wang's co-authors include Thomas A. Cooper, George E. Taffet, Mariella De Biasi, Debra L. Kearney, Ting‐Yu Kuo, Yi‐Ping Hsueh, Ting‐Fang Wang, Muge N. Kuyumcu‐Martinez, Tzyy‐Nan Huang and Xander H.T. Wehrens and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Clinical Investigation and Neuron.

In The Last Decade

Guey‐Shin Wang

17 papers receiving 1.8k citations

Hit Papers

Splicing in disease: disruption of the splicing code and ... 2007 2026 2013 2019 2007 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guey‐Shin Wang Taiwan 12 1.5k 550 182 169 143 17 1.8k
Sharan Paul United States 19 1.0k 0.7× 688 1.3× 81 0.4× 95 0.6× 59 0.4× 37 1.2k
Julia Rankin United Kingdom 21 1.1k 0.7× 560 1.0× 314 1.7× 106 0.6× 49 0.3× 39 1.7k
Grace M. Hobson United States 23 851 0.6× 246 0.4× 297 1.6× 79 0.5× 79 0.6× 42 1.2k
Julio Amigo United States 13 1.2k 0.8× 254 0.5× 226 1.2× 196 1.2× 387 2.7× 20 1.9k
Akira Tohgo Japan 21 1.2k 0.8× 489 0.9× 135 0.7× 87 0.5× 67 0.5× 26 2.3k
Brent L. Fogel United States 21 1.1k 0.7× 717 1.3× 408 2.2× 35 0.2× 72 0.5× 64 1.6k
Becky Liu United Kingdom 12 1.1k 0.7× 394 0.7× 121 0.7× 46 0.3× 34 0.2× 23 1.6k
Sika Zheng United States 23 1.5k 1.0× 154 0.3× 131 0.7× 47 0.3× 342 2.4× 39 1.7k
Emil Ylikallio Finland 20 952 0.6× 287 0.5× 106 0.6× 42 0.2× 33 0.2× 37 1.2k
Delphine Bouhy Belgium 13 872 0.6× 323 0.6× 31 0.2× 67 0.4× 206 1.4× 19 1.4k

Countries citing papers authored by Guey‐Shin Wang

Since Specialization
Citations

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

Fields of papers citing papers by Guey‐Shin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guey‐Shin Wang. 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 Guey‐Shin Wang. The network helps show where Guey‐Shin Wang may publish in the future.

Co-authorship network of co-authors of Guey‐Shin Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Guey‐Shin Wang. A scholar is included among the top collaborators of Guey‐Shin Wang 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 Guey‐Shin Wang. Guey‐Shin Wang 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.
Kuo, Ting‐Yu, et al.. (2023). Loss of MBNL1-mediated retrograde BDNF signaling in the myotonic dystrophy brain. Acta Neuropathologica Communications. 11(1). 44–44. 1 indexed citations
2.
Lin, Chien‐Yu, Yu-Mei Lin, Luc Buée, et al.. (2022). Calpain-2 Mediates MBNL2 Degradation and a Developmental RNA Processing Program in Neurodegeneration. Journal of Neuroscience. 42(25). 5102–5114. 10 indexed citations
3.
Lin, Yu‐Mei, et al.. (2021). CELF1 promotes vascular endothelial growth factor degradation resulting in impaired microvasculature in heart failure. The FASEB Journal. 35(5). e21512–e21512. 10 indexed citations
4.
Lin, Yu-Mei, et al.. (2018). Ubiquitination of MBNL1 Is Required for Its Cytoplasmic Localization and Function in Promoting Neurite Outgrowth. Cell Reports. 22(9). 2294–2306. 39 indexed citations
5.
Lin, Yu-Mei, et al.. (2017). Reduced cytoplasmic MBNL1 is an early event in a brain-specific mouse model of myotonic dystrophy. Human Molecular Genetics. 26(12). 2247–2257. 28 indexed citations
6.
Cheng, Ching‐Feng, et al.. (2017). CELF1 Mediates Connexin 43 mRNA Degradation in Dilated Cardiomyopathy. Circulation Research. 121(10). 1140–1152. 36 indexed citations
7.
Verma, Sunil K., Vaibhav Deshmukh, Patrick Liu, et al.. (2013). Reactivation of Fetal Splicing Programs in Diabetic Hearts Is Mediated by Protein Kinase C Signaling. Journal of Biological Chemistry. 288(49). 35372–35386. 51 indexed citations
8.
Wang, Guey‐Shin, Muge N. Kuyumcu‐Martinez, Satyam Sarma, et al.. (2009). PKC inhibition ameliorates the cardiac phenotype in a mouse model of myotonic dystrophy type 1. Journal of Clinical Investigation. 119(12). 3797–3806. 84 indexed citations
9.
Wang, Guey‐Shin, et al.. (2007). Increased Steady-State Levels of CUGBP1 in Myotonic Dystrophy 1 Are Due to PKC-Mediated Hyperphosphorylation. Molecular Cell. 28(1). 68–78. 352 indexed citations
10.
Wang, Guey‐Shin, Debra L. Kearney, Mariella De Biasi, George E. Taffet, & Thomas A. Cooper. (2007). Elevation of RNA-binding protein CUGBP1 is an early event in an inducible heart-specific mouse model of myotonic dystrophy. Journal of Clinical Investigation. 117(10). 2802–2811. 157 indexed citations
11.
Wang, Guey‐Shin & Thomas A. Cooper. (2007). Splicing in disease: disruption of the splicing code and the decoding machinery. Nature Reviews Genetics. 8(10). 749–761. 764 indexed citations breakdown →
12.
Lin, Chia‐Wen, et al.. (2005). Neural activity‐ and development‐dependent expression and distribution of CASK interacting nucleosome assembly protein in mouse brain. The Journal of Comparative Neurology. 494(4). 606–619. 22 indexed citations
13.
Wang, Ting‐Fang, Guey‐Shin Wang, Yi‐Ling Lin, et al.. (2004). Identification of Tbr‐1/CASK complex target genes in neurons. Journal of Neurochemistry. 91(6). 1483–1492. 69 indexed citations
14.
Wang, Guey‐Shin, Chen‐Jei Hong, Hsin‐Yi Huang, et al.. (2004). Transcriptional Modification by a CASK-Interacting Nucleosome Assembly Protein. Neuron. 43(3). 437–437. 5 indexed citations
15.
Wang, Guey‐Shin, Chen‐Jei Hong, Hsin‐Yi Huang, et al.. (2004). Transcriptional Modification by a CASK-Interacting Nucleosome Assembly Protein. Neuron. 42(1). 113–128. 126 indexed citations
16.
Wang, Guey‐Shin, Nan-Chi A. Chang, Shinn‐Chih Wu, & Alice Chien Chang. (2002). Regulated expression of α2B adrenoceptor during development. Developmental Dynamics. 225(2). 142–152. 17 indexed citations
17.
Wang, Guey‐Shin, et al.. (1999). Vitamin B6 deficiency decreases the glucose utilization in cognitive brain structures of rats. The Journal of Nutritional Biochemistry. 10(9). 525–531. 11 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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