Shu-Ching Wang

1.2k total citations
26 papers, 1.0k citations indexed

About

Shu-Ching Wang is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Shu-Ching Wang has authored 26 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 7 papers in Genetics and 6 papers in Cellular and Molecular Neuroscience. Recurrent topics in Shu-Ching Wang's work include Nuclear Receptors and Signaling (6 papers), Adipose Tissue and Metabolism (4 papers) and Epigenetics and DNA Methylation (3 papers). Shu-Ching Wang is often cited by papers focused on Nuclear Receptors and Signaling (6 papers), Adipose Tissue and Metabolism (4 papers) and Epigenetics and DNA Methylation (3 papers). Shu-Ching Wang collaborates with scholars based in Australia, Taiwan and United States. Shu-Ching Wang's co-authors include George E.O. Muscat, Stephen Myers, Rebecca L. Fitzsimmons, Natalie A. Eriksson, Brett Hosking, Patrick Lau, Ronald M. Evans, Peter J. Bailey, Uwe Dressel and Michael Downes and has published in prestigious journals such as Journal of Biological Chemistry, Biochemical Journal and Biochemical and Biophysical Research Communications.

In The Last Decade

Shu-Ching Wang

26 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
Shu-Ching Wang Australia 18 645 197 177 173 106 26 1.0k
Leonor Pérez‐Martínez Mexico 18 538 0.8× 154 0.8× 172 1.0× 136 0.8× 75 0.7× 51 1.1k
Mauricio Di Fulvio United States 20 716 1.1× 223 1.1× 175 1.0× 82 0.5× 86 0.8× 46 1.1k
Zhennan Lai United States 17 558 0.9× 270 1.4× 216 1.2× 236 1.4× 47 0.4× 24 1.1k
Takatoshi Soga Japan 13 773 1.2× 235 1.2× 237 1.3× 110 0.6× 106 1.0× 21 1.5k
Melanie C MacNicol United States 20 727 1.1× 132 0.7× 121 0.7× 87 0.5× 53 0.5× 41 1.1k
Motoyoshi Sakaue Japan 16 902 1.4× 165 0.8× 186 1.1× 64 0.4× 117 1.1× 35 1.4k
Christine Crumbley United States 10 409 0.6× 205 1.0× 115 0.6× 156 0.9× 123 1.2× 15 997
Jörg Isensee Germany 16 551 0.9× 169 0.9× 162 0.9× 355 2.1× 153 1.4× 28 1.1k
Marit Pedersen Delghandi Norway 7 609 0.9× 107 0.5× 205 1.2× 96 0.6× 158 1.5× 9 1.0k
Benjamin C. Lin United States 11 628 1.0× 77 0.4× 78 0.4× 245 1.4× 161 1.5× 14 1.1k

Countries citing papers authored by Shu-Ching Wang

Since Specialization
Citations

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

Fields of papers citing papers by Shu-Ching Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shu-Ching 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 Shu-Ching Wang. The network helps show where Shu-Ching Wang may publish in the future.

Co-authorship network of co-authors of Shu-Ching Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Shu-Ching Wang. A scholar is included among the top collaborators of Shu-Ching 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 Shu-Ching Wang. Shu-Ching Wang 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
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Tuong, Zewen Kelvin, Rebecca L. Fitzsimmons, Shu-Ching Wang, et al.. (2016). Transgenic Adipose-specific Expression of the Nuclear Receptor RORα Drives a Striking Shift in Fat Distribution and Impairs Glycemic Control. EBioMedicine. 11. 101–117. 3 indexed citations
4.
Oh, Tae Gyu, Shu-Ching Wang, Bipul R. Acharya, et al.. (2016). The Nuclear Receptor, RORγ, Regulates Pathways Necessary for Breast Cancer Metastasis. EBioMedicine. 6. 59–72. 37 indexed citations
5.
Oh, Tae Gyu, Shu-Ching Wang, & George E.O. Muscat. (2016). Minireview: Therapeutic Implications of Epigenetic Signaling in Breast Cancer. Endocrinology. 158(3). en.2016–1716. 13 indexed citations
6.
Liu, Chuanchuan, Shu-Ching Wang, Ruey-Kuen Hsieh, et al.. (2014). B cells facilitate platelet production mediated by cytokines in patients with essential thrombocythaemia. Thrombosis and Haemostasis. 112(9). 537–550. 6 indexed citations
7.
Pearen, Michael A., Rebecca L. Fitzsimmons, Natalie A. Eriksson, et al.. (2013). Transgenic Muscle-Specific Nor-1 Expression Regulates Multiple Pathways That Effect Adiposity, Metabolism, and Endurance. Molecular Endocrinology. 27(11). 1897–1917. 54 indexed citations
8.
Wang, Shu-Ching & George E.O. Muscat. (2013). Nuclear receptors and epigenetic signaling: Novel regulators of glycogen metabolism in skeletal muscle. IUBMB Life. 65(8). 657–664. 10 indexed citations
9.
Wang, Shu-Ching, et al.. (2011). College students' stress under current economic downturn.. College student journal. 45(3). 536. 45 indexed citations
10.
Wang, Shu-Ching, Stephen Myers, Natalie A. Eriksson, Rebecca L. Fitzsimmons, & George E.O. Muscat. (2011). Nr4a1 siRNA Expression Attenuates α-MSH Regulated Gene Expression in 3T3-L1 Adipocytes. Molecular Endocrinology. 25(2). 291–306. 17 indexed citations
11.
Myers, Stephen, et al.. (2009). β-Adrenergic signaling regulates NR4A nuclear receptor and metabolic gene expression in multiple tissues. Molecular and Cellular Endocrinology. 309(1-2). 101–108. 70 indexed citations
12.
Wang, Shu-Ching, et al.. (2009). An ERRβ/γ agonist modulates GRα expression, and glucocorticoid responsive gene expression in skeletal muscle cells. Molecular and Cellular Endocrinology. 315(1-2). 146–152. 31 indexed citations
13.
Lau, Patrick, et al.. (2008). The Orphan Nuclear Receptor, RORα, Regulates Gene Expression That Controls Lipid Metabolism. Journal of Biological Chemistry. 283(26). 18411–18421. 156 indexed citations
14.
Myers, Stephen, Shu-Ching Wang, & George E.O. Muscat. (2006). The Chicken Ovalbumin Upstream Promoter-Transcription Factors Modulate Genes and Pathways Involved in Skeletal Muscle Cell Metabolism. Journal of Biological Chemistry. 281(34). 24149–24160. 41 indexed citations
15.
Jan, Chung‐Ren, Ching‐Hsein Chen, Shu-Ching Wang, & Soong‐Yu Kuo. (2004). Effect of methylglyoxal on intracellular calcium levels and viability in renal tubular cells. Cellular Signalling. 17(7). 847–855. 47 indexed citations
16.
Hosking, Brett, Shu-Ching Wang, Meredith Downes, Peter Koopman, & George E.O. Muscat. (2004). The VCAM-1 Gene That Encodes the Vascular Cell Adhesion Molecule Is a Target of the Sry-related High Mobility Group Box Gene, Sox18. Journal of Biological Chemistry. 279(7). 5314–5322. 41 indexed citations
17.
Chen, Shen Liang, Shu-Ching Wang, Brett Hosking, & George E.O. Muscat. (2001). Subcellular Localization of the Steroid Receptor Coactivators (SRCs) and MEF2 in Muscle and Rhabdomyosarcoma Cells. Molecular Endocrinology. 15(5). 783–796. 42 indexed citations
18.
Dressel, Uwe, Peter J. Bailey, Shu-Ching Wang, et al.. (2001). A Dynamic Role for HDAC7 in MEF2-mediated Muscle Differentiation. Journal of Biological Chemistry. 276(20). 17007–17013. 172 indexed citations
19.
Hosking, Brett, et al.. (2001). Cloning and functional analysis of the Sry -related HMG box gene, Sox18. Gene. 262(1-2). 239–247. 37 indexed citations
20.
Hosking, Brett, et al.. (2001). SOX18 Directly Interacts with MEF2C in Endothelial Cells. Biochemical and Biophysical Research Communications. 287(2). 493–500. 43 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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