Y. Eric Shi

1.1k total citations
22 papers, 897 citations indexed

About

Y. Eric Shi is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Y. Eric Shi has authored 22 papers receiving a total of 897 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 10 papers in Cancer Research and 7 papers in Oncology. Recurrent topics in Y. Eric Shi's work include Protease and Inhibitor Mechanisms (8 papers), Blood Coagulation and Thrombosis Mechanisms (5 papers) and Peptidase Inhibition and Analysis (4 papers). Y. Eric Shi is often cited by papers focused on Protease and Inhibitor Mechanisms (8 papers), Blood Coagulation and Thrombosis Mechanisms (5 papers) and Peptidase Inhibition and Analysis (4 papers). Y. Eric Shi collaborates with scholars based in United States, China and India. Y. Eric Shi's co-authors include Yiliang E. Liu, Ming‐Sheng Wang, Itzhak D. Goldberg, Yangfu Jiang, Qing‐Xiang Amy Sang, J. M. Greene, Shijie Sheng, Heather F. Bigg, Bjorn Steffensen and Christopher M. Overall and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and PLoS ONE.

In The Last Decade

Y. Eric Shi

21 papers receiving 885 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Y. Eric Shi United States 15 413 406 330 157 114 22 897
Shou‐Ih Hu United States 10 260 0.6× 387 1.0× 239 0.7× 50 0.3× 27 0.2× 10 836
Olivier Robledo Canada 14 200 0.5× 299 0.7× 355 1.1× 35 0.2× 55 0.5× 21 856
Omar Benzakour United Kingdom 15 177 0.4× 382 0.9× 101 0.3× 141 0.9× 80 0.7× 33 869
F. van Valen Germany 22 221 0.5× 844 2.1× 491 1.5× 56 0.4× 96 0.8× 47 1.5k
Lucia Cappabianca Italy 19 282 0.7× 570 1.4× 185 0.6× 43 0.3× 73 0.6× 45 944
Kay M. Southgate United Kingdom 14 364 0.9× 335 0.8× 142 0.4× 206 1.3× 68 0.6× 18 1.2k
Jan‐Marcus Daniel Germany 15 191 0.5× 609 1.5× 99 0.3× 92 0.6× 40 0.4× 31 992
Martine Charbonneau Canada 14 266 0.6× 397 1.0× 178 0.5× 49 0.3× 46 0.4× 19 827
Masahiro Sato Japan 7 219 0.5× 739 1.8× 231 0.7× 44 0.3× 90 0.8× 26 1.0k
Antonella Tacconelli Italy 15 327 0.8× 487 1.2× 232 0.7× 58 0.4× 66 0.6× 24 820

Countries citing papers authored by Y. Eric Shi

Since Specialization
Citations

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

Fields of papers citing papers by Y. Eric Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Y. Eric Shi. 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 Y. Eric Shi. The network helps show where Y. Eric Shi may publish in the future.

Co-authorship network of co-authors of Y. Eric Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Y. Eric Shi. A scholar is included among the top collaborators of Y. Eric Shi 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 Y. Eric Shi. Y. Eric Shi 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.
Qin, X. S., Y. Eric Shi, Shang‐Qin Zeng, et al.. (2025). A nematode effector hijacks a host RBR-type E3 ubiquitin ligase to regulate NRC4 receptor-mediated plant immunity and facilitate parasitism. The Plant Cell. 37(7). 3 indexed citations
2.
Chen, Hongyin, Yun Dong, Yunyun Su, et al.. (2024). Integrative omics analysis identifies biomarkers of septic cardiomyopathy. PLoS ONE. 19(11). e0310412–e0310412.
3.
Zheng, Weitao, et al.. (2024). Exploring the therapeutic potential of precision T-Cell Receptors (TCRs) in targeting KRAS G12D cancer through <i>in vitro</i> development. Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics. 32(12). 1837–1850. 4 indexed citations
4.
Liang, Wei, Shuying Miao, Shuai He, et al.. (2014). Synuclein γ protects Akt and mTOR and renders tumor resistance to Hsp90 disruption. Oncogene. 34(18). 2398–2405. 22 indexed citations
5.
Shao, Yongfeng, et al.. (2014). Synuclein gamma protects HER2 and renders resistance to Hsp90 disruption. Molecular Oncology. 8(8). 1521–1531. 7 indexed citations
6.
Wu, Kejin, Shuo Huang, Mingjie Zhu, et al.. (2013). Expression of synuclein gamma indicates poor prognosis of triple-negative breast cancer. Medical Oncology. 30(3). 612–612. 14 indexed citations
7.
Liu, Caiyun, Bin Dong, Aiping Lu, et al.. (2010). Synuclein gamma predicts poor clinical outcome in colon cancer with normal levels of carcinoembryonic antigen. BMC Cancer. 10(1). 359–359. 30 indexed citations
8.
9.
Wu, Kejin, Zhiwei Quan, Yichu Zhang, et al.. (2006). Expression of neuronal protein synuclein gamma gene as a novel marker for breast cancer prognosis. Breast Cancer Research and Treatment. 101(3). 259–267. 51 indexed citations
10.
Jiang, Yangfu, et al.. (2006). Chaperoning of estrogen receptor and induction of mammary gland proliferation by neuronal protein synuclein gamma. Oncogene. 26(14). 2115–2125. 24 indexed citations
11.
12.
Wang, Ming‐Sheng, Yiliang E. Liu, Itzhak D. Goldberg, & Y. Eric Shi. (2003). Induction of Mammary Gland Differentiation in Transgenic Mice by the Fatty Acid-binding Protein MRG. Journal of Biological Chemistry. 278(47). 47319–47325. 11 indexed citations
13.
Jiang, Yangfu, Aiping Lü, Anu Gupta, et al.. (2003). Stimulation of estrogen receptor signaling by gamma synuclein.. PubMed. 63(14). 3899–903. 56 indexed citations
14.
Celiker, Mahmut, Nungavaram S. Ramamurthy, Jin‐Wen Xu, et al.. (2002). Inhibition of adjuvant‐induced arthritis by systemic tissue inhibitor of metalloproteinases 4 gene delivery. Arthritis & Rheumatism. 46(12). 3361–3368. 30 indexed citations
15.
Liu, Jingwen, et al.. (2000). Transcriptional suppression of synuclein γ (SNCG) expression in human breast cancer cells by the growth inhibitory cytokine oncostatin M. Breast Cancer Research and Treatment. 62(2). 99–107. 39 indexed citations
16.
Dollery, C, Jean R. McEwan, Ming‐Sheng Wang, et al.. (1999). TIMP‐4 Is Regulated by Vascular Injury in Rats. Annals of the New York Academy of Sciences. 878(1). 740–741. 9 indexed citations
17.
Liu, Yiliang E., Ming‐Sheng Wang, J. M. Greene, et al.. (1997). Preparation and Characterization of Recombinant Tissue Inhibitor of Metalloproteinase 4 (TIMP-4). Journal of Biological Chemistry. 272(33). 20479–20483. 110 indexed citations
18.
Wang, Ming‐Sheng, J. M. Greene, Shijie Sheng, et al.. (1997). Inhibition of tumor growth and metastasis of human breast cancer cells transfected with tissue inhibitor of metalloproteinase 4. Oncogene. 14(23). 2767–2774. 135 indexed citations
19.
Bigg, Heather F., Y. Eric Shi, Yiliang E. Liu, Bjorn Steffensen, & Christopher M. Overall. (1997). Specific, High Affinity Binding of Tissue Inhibitor of Metalloproteinases-4 (TIMP-4) to the COOH-terminal Hemopexin-like Domain of Human Gelatinase A. Journal of Biological Chemistry. 272(24). 15496–15500. 127 indexed citations
20.
Shi, Y. Eric, et al.. (1995). Stromal—epithelial interaction in type IV collagenase expression and activation: The role in cancer metastasis. Proceedings of the Fourth International Symposium on Polarization Phenomena in Nuclear Reactions. 74. 215–234. 3 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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