Gui‐Ping Xu

898 total citations
26 papers, 745 citations indexed

About

Gui‐Ping Xu is a scholar working on Molecular Biology, Oncology and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Gui‐Ping Xu has authored 26 papers receiving a total of 745 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Oncology and 4 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Gui‐Ping Xu's work include interferon and immune responses (3 papers), Retinoids in leukemia and cellular processes (3 papers) and Growth Hormone and Insulin-like Growth Factors (3 papers). Gui‐Ping Xu is often cited by papers focused on interferon and immune responses (3 papers), Retinoids in leukemia and cellular processes (3 papers) and Growth Hormone and Insulin-like Growth Factors (3 papers). Gui‐Ping Xu collaborates with scholars based in China, Singapore and Macao. Gui‐Ping Xu's co-authors include Nikola P. Pavletich, Philip D. Jeffrey, J. Wade Harper, Guoliang Wu, Brenda A. Schulman, Yanchun Liang, Xiaohu Shi, Chunguo Wu, Heow Pueh Lee and Hongwei Ge and has published in prestigious journals such as PLoS ONE, Biochemical and Biophysical Research Communications and Experimental Cell Research.

In The Last Decade

Gui‐Ping Xu

24 papers receiving 732 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gui‐Ping Xu China 10 402 129 103 84 73 26 745
Hong Guo China 19 497 1.2× 120 0.9× 126 1.2× 24 0.3× 163 2.2× 90 1.2k
Qiang Kang China 21 345 0.9× 231 1.8× 200 1.9× 111 1.3× 217 3.0× 65 1.1k
Shousheng Liu China 19 229 0.6× 113 0.9× 89 0.9× 107 1.3× 133 1.8× 54 1.1k
Alain Coletta Belgium 9 760 1.9× 266 2.1× 69 0.7× 51 0.6× 124 1.7× 12 1.2k
Xianglei He China 16 249 0.6× 88 0.7× 144 1.4× 105 1.3× 95 1.3× 51 965
Fei He China 17 554 1.4× 92 0.7× 23 0.2× 62 0.7× 72 1.0× 86 1.2k
Giosuè Lo Bosco Italy 17 559 1.4× 173 1.3× 50 0.5× 19 0.2× 95 1.3× 76 1.1k
Che Lin Taiwan 16 209 0.5× 133 1.0× 75 0.7× 17 0.2× 75 1.0× 71 946
Weixing Feng China 21 536 1.3× 43 0.3× 122 1.2× 71 0.8× 126 1.7× 88 996
Hiro Takahashi Japan 22 918 2.3× 44 0.3× 126 1.2× 35 0.4× 96 1.3× 83 1.3k

Countries citing papers authored by Gui‐Ping Xu

Since Specialization
Citations

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

Fields of papers citing papers by Gui‐Ping Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gui‐Ping Xu. 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 Gui‐Ping Xu. The network helps show where Gui‐Ping Xu may publish in the future.

Co-authorship network of co-authors of Gui‐Ping Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Gui‐Ping Xu. A scholar is included among the top collaborators of Gui‐Ping Xu 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 Gui‐Ping Xu. Gui‐Ping Xu 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.
Xia, Di, et al.. (2024). Targeting inhibition of TCTP could inhibit proliferation and induce apoptosis in AML cells. Cellular Signalling. 117. 111074–111074.
2.
3.
Wu, Lifang, Gui‐Ping Xu, Qing Zhao, et al.. (2020). The association between adiponectin gene rs182052 polymorphism and cancer risk: a meta-analysis. Bioscience Reports. 40(6). 1 indexed citations
4.
Wu, Lifang, Gui‐Ping Xu, Qing Zhao, et al.. (2019). The association between hypoxia inducible factor 1 subunit alpha gene rs2057482 polymorphism and cancer risk: a meta-analysis. BMC Cancer. 19(1). 1123–1123. 7 indexed citations
5.
Tang, Chuan, Gui‐Ping Xu, Chunguo Wu, et al.. (2019). Surprisingly Popular Algorithm-Based Comprehensive Adaptive Topology Learning PSO. 2603–2610. 9 indexed citations
6.
Xu, Gui‐Ping, Weixian Chen, Qing Zhao, et al.. (2019). Association between the insulin-like growth factor 1 gene rs2195239 and rs2162679 polymorphisms and cancer risk: a meta-analysis. BMC Medical Genetics. 20(1). 17–17. 7 indexed citations
7.
Zong, Xiaofeng, Fuke Wu, & Gui‐Ping Xu. (2018). Convergence and stability of two classes of theta-Milstein schemes for stochastic differential equations. Journal of Computational and Applied Mathematics. 336. 8–29. 19 indexed citations
8.
He, Bing, Yanyan Chang, Chao Yang, et al.. (2018). Adenylate cyclase 7 regulated by miR-192 promotes ATRA-induced differentiation of acute promyelocytic leukemia cells. Biochemical and Biophysical Research Communications. 506(3). 543–547. 8 indexed citations
9.
Xu, Gui‐Ping, Weixian Chen, Wenyue Xie, & Lifang Wu. (2018). The association between IGF1 gene rs1520220 polymorphism and cancer susceptibility: a meta-analysis based on 12,884 cases and 58,304 controls. Environmental Health and Preventive Medicine. 23(1). 38–38. 5 indexed citations
10.
Xu, Gui‐Ping, Qing Zhao, Ding Wang, et al.. (2018). The association between BRCA1 gene polymorphism and cancer risk: a meta-analysis. Oncotarget. 9(9). 8681–8694. 17 indexed citations
11.
Xu, Gui‐Ping, Weixian Chen, Wenyue Xie, & Lifang Wu. (2018). The association between IGF1 Gene 3’-UTR polymorphisms and cancer risk. Medicine. 97(51). e13829–e13829. 5 indexed citations
12.
13.
Tian, Yaohua, Lijun Shen, Jian Wu, et al.. (2015). Sleep duration and timing in relation to osteoporosis in an elderly Chinese population: a cross-sectional analysis in the Dongfeng–Tongji cohort study. Osteoporosis International. 26(11). 2641–2648. 36 indexed citations
14.
Xu, Gui‐Ping, Lifang Wu, Jingjing Li, et al.. (2015). Performance Assessment of Internal Quality Control (IQC) Products in Blood Transfusion Compatibility Testing in China. PLoS ONE. 10(10). e0141145–e0141145. 6 indexed citations
15.
Zhuang, Likun, et al.. (2014). MicroRNA-181a-mediated downregulation of AC9 protein decreases intracellular cAMP level and inhibits ATRA-induced APL cell differentiation. Cell Death and Disease. 5(4). e1161–e1161. 24 indexed citations
16.
Chen, Weixian, Jun Zhang, Gang Lǚ, et al.. (2014). Development of an immunochromatographic lateral flow device for rapid diagnosis of Vibrio cholerae O1 serotype Ogawa. Clinical Biochemistry. 47(6). 448–454. 31 indexed citations
17.
Xu, Gui‐Ping, et al.. (2013). Rig-G negatively regulates SCF-E3 ligase activities by disrupting the assembly of COP9 signalosome complex. Biochemical and Biophysical Research Communications. 432(3). 425–430. 11 indexed citations
18.
Pan, Xiaorong, et al.. (2012). [Role of auto-secreted interferon α in all-trans retinoic acid-induced expression of RIG-G gene].. PubMed. 92(2). 124–7. 2 indexed citations
19.
Pan, Xiaorong, et al.. (2010). Intact JAK–STAT signaling pathway is a prerequisite for STAT1 to reinforce the expression of RIG-G gene. Experimental Cell Research. 317(4). 513–520. 10 indexed citations
20.
Wu, Guoliang, Gui‐Ping Xu, Brenda A. Schulman, et al.. (2003). STRUCTURE OF A BETA-TRCPI-SKP1-BETA-CATENIN COMPLEX: DESTRUCTION MOTIF BINDING AND LYSINE SPECIFICITY OF THE SCF(BETA-TRCP1) UBIQUITIN LIGASE. Molecules and Cells. 6. 315 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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