Shur‐Jen Wang

11.7k total citations
45 papers, 1.9k citations indexed

About

Shur‐Jen Wang is a scholar working on Molecular Biology, Genetics and Cell Biology. According to data from OpenAlex, Shur‐Jen Wang has authored 45 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 11 papers in Genetics and 6 papers in Cell Biology. Recurrent topics in Shur‐Jen Wang's work include Bioinformatics and Genomic Networks (22 papers), Biomedical Text Mining and Ontologies (13 papers) and Gene expression and cancer classification (11 papers). Shur‐Jen Wang is often cited by papers focused on Bioinformatics and Genomic Networks (22 papers), Biomedical Text Mining and Ontologies (13 papers) and Gene expression and cancer classification (11 papers). Shur‐Jen Wang collaborates with scholars based in United States, Canada and United Kingdom. Shur‐Jen Wang's co-authors include Wajeeh Saadi, Francis Lin, Noo Li Jeon, Mark Aitkenhead, Melinda R. Dwinell, Christopher C.W. Hughes, Jennifer R. Smith, Stanley J. F. Laulederkind, G. Thomas Hayman and Mary Shimoyama and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Stroke.

In The Last Decade

Shur‐Jen Wang

44 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shur‐Jen Wang United States 21 848 642 275 213 191 45 1.9k
Sigrun Gustafsdottir Sweden 18 2.0k 2.4× 578 0.9× 259 0.9× 171 0.8× 106 0.6× 22 2.7k
Karen Schmeichel United States 13 924 1.1× 296 0.5× 427 1.6× 333 1.6× 119 0.6× 20 1.7k
Cynthia L. Stokes United States 18 663 0.8× 600 0.9× 323 1.2× 256 1.2× 53 0.3× 30 1.5k
Satoru Ito Japan 21 848 1.0× 184 0.3× 235 0.9× 134 0.6× 91 0.5× 43 1.6k
James R. W. Conway Australia 20 826 1.0× 280 0.4× 396 1.4× 375 1.8× 77 0.4× 33 1.6k
Shuling Guo United States 21 1.6k 1.9× 307 0.5× 938 3.4× 149 0.7× 149 0.8× 58 2.7k
Yuming Liu United States 20 410 0.5× 357 0.6× 269 1.0× 469 2.2× 58 0.3× 68 1.7k
Graham Wright Singapore 23 968 1.1× 228 0.4× 331 1.2× 123 0.6× 99 0.5× 79 1.8k
John G. Lock Australia 23 1.1k 1.3× 305 0.5× 1.0k 3.7× 219 1.0× 98 0.5× 43 2.0k
Vasiliy S. Chernyshev Russia 13 1.0k 1.2× 365 0.6× 173 0.6× 115 0.5× 49 0.3× 38 1.8k

Countries citing papers authored by Shur‐Jen Wang

Since Specialization
Citations

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

Fields of papers citing papers by Shur‐Jen Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shur‐Jen Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Shur‐Jen Wang. A scholar is included among the top collaborators of Shur‐Jen 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 Shur‐Jen Wang. Shur‐Jen 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
1.
Smith, Jennifer R., Marek Tutaj, Jyothi Thota, et al.. (2025). Standardized pipelines support and facilitate integration of diverse datasets at the Rat Genome Database. Database. 2025.
2.
Vedi, Mahima, Jennifer R. Smith, G. Thomas Hayman, et al.. (2023). 2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource. Genetics. 224(1). 27 indexed citations
3.
Kaldunski, Mary L., Jennifer R. Smith, Jeffrey L De Pons, et al.. (2023). Rare disease research resources at the Rat Genome Database. Genetics. 224(4). 2 indexed citations
4.
Wang, Shur‐Jen, Jeffrey L De Pons, Wendy Demos, et al.. (2022). Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal. Genes. 13(12). 2304–2304. 1 indexed citations
5.
Laulederkind, Stanley J. F., G. Thomas Hayman, Shur‐Jen Wang, et al.. (2019). Rat Genome Databases, Repositories, and Tools. Methods in molecular biology. 2018. 71–96. 14 indexed citations
6.
Laulederkind, Stanley J. F., G. Thomas Hayman, Shur‐Jen Wang, et al.. (2018). A Primer for the Rat Genome Database (RGD). Methods in molecular biology. 1757. 163–209. 7 indexed citations
7.
Hayman, G. Thomas, Stanley J. F. Laulederkind, Jennifer R. Smith, et al.. (2016). The Disease Portals, disease–gene annotation and the RGD disease ontology at the Rat Genome Database. Database. 2016. baw034–baw034. 22 indexed citations
8.
Petri, Victoria, G. Thomas Hayman, Marek Tutaj, et al.. (2015). Disease, Models, Variants and Altered Pathways—Journeying RGD Through the Magnifying Glass. Computational and Structural Biotechnology Journal. 14. 35–48. 3 indexed citations
9.
Shimoyama, Mary, Jeff De Pons, G. Thomas Hayman, et al.. (2014). The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease. Nucleic Acids Research. 43(D1). D743–D750. 164 indexed citations
10.
Yang, Fan, Shuo Liu, Shur‐Jen Wang, et al.. (2011). Tissue Plasminogen Activator Expression and Barrier Properties of Human Brain Microvascular Endothelial Cells. Cellular Physiology and Biochemistry. 28(4). 631–638. 20 indexed citations
11.
Shimoyama, Mary, Jennifer R. Smith, Rajni Nigam, et al.. (2011). RGD: A comparative genomics platform. Human Genomics. 5(2). 124–9. 20 indexed citations
12.
Shimoyama, Mary, G. Thomas Hayman, Stanley J. F. Laulederkind, et al.. (2009). The Rat Genome Database Curators: Who, What, Where, Why. PLoS Computational Biology. 5(11). e1000582–e1000582. 21 indexed citations
13.
Lin, Francis, et al.. (2005). Neutrophil Migration in Opposing Chemoattractant Gradients Using Microfluidic Chemotaxis Devices. Annals of Biomedical Engineering. 33(4). 475–482. 85 indexed citations
14.
Lin, Francis, et al.. (2004). Generation of dynamic temporal and spatial concentration gradients using microfluidic devices. Lab on a Chip. 4(3). 164–164. 178 indexed citations
15.
Wang, Shur‐Jen, et al.. (2004). Differential effects of EGF gradient profiles on MDA-MB-231 breast cancer cell chemotaxis. Experimental Cell Research. 300(1). 180–189. 217 indexed citations
16.
Lin, Francis, et al.. (2004). Effective neutrophil chemotaxis is strongly influenced by mean IL-8 concentration. Biochemical and Biophysical Research Communications. 319(2). 576–581. 121 indexed citations
17.
Aitkenhead, Mark, et al.. (2002). Identification of Endothelial Cell Genes Expressed in an in Vitro Model of Angiogenesis: Induction of ESM-1, βig-h3, and NrCAM. Microvascular Research. 63(2). 159–171. 127 indexed citations
18.
Plendl, Johanna, Barbara Gilligan, Shur‐Jen Wang, et al.. (2002). PRIMITIVE ENDOTHELIAL CELL LINES FROM THE PORCINE EMBRYONIC YOLK SAC. In Vitro Cellular & Developmental Biology - Animal. 38(6). 334–334. 13 indexed citations
19.
Wang, Shur‐Jen, et al.. (2001). The Basic Helix-Loop-Helix Transcription Factor HESR1 Regulates Endothelial Cell Tube Formation. Journal of Biological Chemistry. 276(9). 6169–6176. 127 indexed citations
20.
Auerbach, Robert, Barbara Gilligan, Lisheng Lü, & Shur‐Jen Wang. (1997). Cell interactions in the mouse yolk sac: Vasculogenesis and hematopoiesis. Journal of Cellular Physiology. 173(2). 202–205. 10 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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