Jue Shi

3.6k total citations
57 papers, 2.0k citations indexed

About

Jue Shi is a scholar working on Molecular Biology, Cell Biology and Oncology. According to data from OpenAlex, Jue Shi has authored 57 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 14 papers in Cell Biology and 12 papers in Oncology. Recurrent topics in Jue Shi's work include Microtubule and mitosis dynamics (11 papers), Cancer-related Molecular Pathways (8 papers) and Cellular Mechanics and Interactions (6 papers). Jue Shi is often cited by papers focused on Microtubule and mitosis dynamics (11 papers), Cancer-related Molecular Pathways (8 papers) and Cellular Mechanics and Interactions (6 papers). Jue Shi collaborates with scholars based in Hong Kong, China and United States. Jue Shi's co-authors include Timothy J. Mitchison, James D. Orth, Yanting Zhu, Tim Mitchison, Philip W. T. Pong, Chun‐Ming Wong, Yuan Zhou, Yuan Zhou, Sung Hoon Jung and Xi Chen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Biological Chemistry.

In The Last Decade

Jue Shi

53 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jue Shi Hong Kong	25	1.3k	666	588	233	226	57	2.0k
John C. Dawson United Kingdom	23	1.3k 1.0×	691 1.0×	504 0.9×	230 1.0×	234 1.0×	46	2.4k
Nicole S. Bryce Australia	23	782 0.6×	747 1.1×	386 0.7×	110 0.5×	234 1.0×	45	1.9k
Alan Serrels United Kingdom	28	1.5k 1.2×	709 1.1×	740 1.3×	366 1.6×	287 1.3×	40	3.3k
Anika Nagelkerke Netherlands	24	1.3k 1.0×	459 0.7×	314 0.5×	491 2.1×	499 2.2×	50	2.3k
Christopher C. DuFort United States	16	888 0.7×	974 1.5×	471 0.8×	166 0.7×	782 3.5×	22	2.6k
Sébastien Harlepp France	24	794 0.6×	395 0.6×	578 1.0×	136 0.6×	519 2.3×	44	1.9k
Karthikeyan Mythreye United States	25	1.2k 0.9×	795 1.2×	438 0.7×	343 1.5×	389 1.7×	62	2.2k
Taranjit S. Gujral United States	22	1.0k 0.8×	263 0.4×	462 0.8×	218 0.9×	418 1.8×	58	1.9k
Verónica Estrella United States	17	1.4k 1.1×	369 0.6×	462 0.8×	679 2.9×	449 2.0×	28	2.5k
Abedelnasser Abulrob Canada	23	1.0k 0.8×	173 0.3×	333 0.6×	182 0.8×	238 1.1×	46	1.9k

Countries citing papers authored by Jue Shi

Since Specialization

Citations

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

Fields of papers citing papers by Jue Shi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jue Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Jue Shi. A scholar is included among the top collaborators of Jue 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 Jue Shi. Jue Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Dai, Daisy, Ting Wang, Ying Wu, et al.. (2025). CDK1-mediated phosphorylation of LDHA fuels mitosis through LDHB-dependent lactate oxidation. EMBO Reports. 26(20). 4923–4949. 1 indexed citations

Yang, Yang, Kecheng Li, Jue Shi, et al.. (2025). Proteogenomics Reveals Microproteins in Activated T Cells. Molecular & Cellular Proteomics. 24(6). 100914–100914. 3 indexed citations

Shi, Jue, et al.. (2025). Preparation and properties of new nano hybrid crude oil pour point depressant. Colloids and Surfaces A Physicochemical and Engineering Aspects. 723. 137411–137411.

Cai, Mingchao, et al.. (2025). Effects of additive noise and variable coefficients on the exact solutions of the stochastic Kawahara equation. Physics Letters A. 554. 130723–130723.

Zhu, Yanting, et al.. (2023). Deep Learning–Based 3D Single-Cell Imaging Analysis Pipeline Enables Quantification of Cell–Cell Interaction Dynamics in the Tumor Microenvironment. Cancer Research. 84(4). 517–526. 9 indexed citations

Tian, Xu, Ting Wang, Dongming Zhang, et al.. (2023). A mitotic NADPH upsurge promotes chromosome segregation and tumour progression in aneuploid cancer cells. Nature Metabolism. 5(7). 1141–1158. 22 indexed citations

Cai, Hongjiao, Jue Shi, Yajing Fu, et al.. (2022). Downregulation of TCF1 in HIV Infection Impairs T-cell Proliferative Capacity by Disrupting Mitochondrial Function. Frontiers in Microbiology. 13. 880873–880873.

Zhu, Yanting & Jue Shi. (2022). Cytotoxic and chemotactic dynamics of NK cells quantified by live-cell imaging. Methods in cell biology. 173. 49–64.

Zhu, Yanting, Jun Xie, & Jue Shi. (2021). Rac1/ROCK-driven membrane dynamics promote natural killer cell cytotoxicity via granzyme-induced necroptosis. BMC Biology. 19(1). 140–140. 14 indexed citations

10.

Du, Yimeng, et al.. (2019). Magnetic iron oxide nanoparticle-hollow mesoporous silica Spheres:Fabrication and potential application in drug delivery. Current Applied Physics. 20(2). 320–325. 38 indexed citations

11.

Shi, Jue, et al.. (2019). Sensitive and Specific Colorimetric Detection of Cancer Cells Based on Folate-Conjugated Gold–Iron-Oxide Composite Nanoparticles. ACS Applied Nano Materials. 2(11). 7421–7431. 26 indexed citations

12.

Huang, Bo, et al.. (2018). Cell type–dependent bimodal p53 activation engenders a dynamic mechanism of chemoresistance. Science Advances. 4(12). eaat5077–eaat5077. 32 indexed citations

13.

Kueh, Hao Yuan, Yanting Zhu, & Jue Shi. (2016). A simplified Bcl-2 network model reveals quantitative determinants of cell-to-cell variation in sensitivity to anti-mitotic chemotherapeutics. Scientific Reports. 6(1). 36585–36585. 4 indexed citations

14.

Yung, Ken Kin Lam, et al.. (2015). Extract of Zuojin Pill (左金丸) induces apoptosis of SGC-7901 cells via mitochondria-dependent pathway. Chinese Journal of Integrative Medicine. 21(11). 837–845. 6 indexed citations

15.

Liang, Jin, et al.. (2013). Resonance versus linear responses to alternating electric fields induce mechanistically distinct mammalian cell death. Bioelectrochemistry. 94. 61–68. 5 indexed citations

16.

Shi, Jue, et al.. (2011). Navitoclax (ABT-263) Accelerates Apoptosis during Drug-Induced Mitotic Arrest by Antagonizing Bcl-xL. Cancer Research. 71(13). 4518–4526. 102 indexed citations

17.

Mitchison, Timothy J., et al.. (2010). Stochastic Competition between Mechanistically Independent Slippage and Death Pathways Determines Cell Fate during Mitotic Arrest. PLoS ONE. 5(12). e15724–e15724. 55 indexed citations

18.

Shi, Jue, et al.. (2009). Evidence that Mitotic Exit Is a Better Cancer Therapeutic Target Than Spindle Assembly. Cancer Cell. 16(4). 347–358. 240 indexed citations

19.

Orth, James D., Jue Shi, Clement T. Loy, et al.. (2008). Quantitative live imaging of cancer and normal cells treated with Kinesin-5 inhibitors indicates significant differences in phenotypic responses and cell fate. Molecular Cancer Therapeutics. 7(11). 3480–3489. 91 indexed citations

20.

Shi, Jue, James D. Orth, & Tim Mitchison. (2008). Cell Type Variation in Responses to Antimitotic Drugs that Target Microtubules and Kinesin-5. Cancer Research. 68(9). 3269–3276. 168 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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