Xing Chen

3.7k total citations
54 papers, 2.8k citations indexed

About

Xing Chen is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Xing Chen has authored 54 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Molecular Biology, 25 papers in Cancer Research and 12 papers in Computational Theory and Mathematics. Recurrent topics in Xing Chen's work include MicroRNA in disease regulation (23 papers), Cancer-related molecular mechanisms research (18 papers) and Bioinformatics and Genomic Networks (15 papers). Xing Chen is often cited by papers focused on MicroRNA in disease regulation (23 papers), Cancer-related molecular mechanisms research (18 papers) and Bioinformatics and Genomic Networks (15 papers). Xing Chen collaborates with scholars based in China, Hong Kong and Canada. Xing Chen's co-authors include Zhu‐Hong You, Yan Zhao, Jun Yin, Huang Li, Li Zhang, Chun-Chun Wang, Guiying Yan, Jianqiang Li, Biao Ren and Ming Chen and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Xing Chen

53 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xing Chen China 32 2.2k 1.3k 790 158 145 54 2.8k
Javier Garcı́a-Garcı́a Spain 18 1.7k 0.8× 275 0.2× 449 0.6× 70 0.4× 51 0.4× 46 2.6k
Ina Koch Germany 26 2.1k 0.9× 298 0.2× 325 0.4× 143 0.9× 35 0.2× 107 3.3k
Xianghong Jasmine Zhou United States 35 3.7k 1.7× 1.1k 0.9× 243 0.3× 31 0.2× 64 0.4× 88 4.7k
Paolo Magni Italy 27 986 0.4× 238 0.2× 175 0.2× 51 0.3× 78 0.5× 136 2.4k
Yijie Ding China 37 3.4k 1.5× 434 0.3× 1.2k 1.5× 247 1.6× 14 0.1× 181 4.3k
Yu‐An Huang China 36 2.6k 1.2× 1.4k 1.1× 603 0.8× 99 0.6× 12 0.1× 134 3.6k
Kai Zheng China 20 845 0.4× 436 0.3× 385 0.5× 119 0.8× 13 0.1× 57 1.3k
Yi Xiong China 32 2.2k 1.0× 233 0.2× 807 1.0× 249 1.6× 15 0.1× 126 3.1k
Jiajie Peng China 26 2.0k 0.9× 599 0.5× 577 0.7× 144 0.9× 12 0.1× 107 2.6k
Juan Liu China 26 1.2k 0.5× 175 0.1× 305 0.4× 123 0.8× 14 0.1× 168 2.2k

Countries citing papers authored by Xing Chen

Since Specialization
Citations

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

Fields of papers citing papers by Xing Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xing Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Xing Chen. A scholar is included among the top collaborators of Xing Chen 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 Xing Chen. Xing Chen 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.
Peng, Lihong, et al.. (2025). Predicting cell–cell communication by combining heterogeneous ensemble deep learning and weighted geometric mean. Applied Soft Computing. 172. 112839–112839. 5 indexed citations
2.
Chen, Xing & Huang Li. (2023). Computational model for disease research. Briefings in Bioinformatics. 24(1). 13 indexed citations
3.
Chen, Xing, et al.. (2021). MiR-133a delivery to osteoblasts ameliorates mechanical unloading-triggered osteopenia progression in vitro and in vivo. International Immunopharmacology. 97. 107613–107613. 7 indexed citations
4.
Xu, Xu, et al.. (2020). The cooperative complex of Argonaute-2 and microRNA-146a regulates hepatitis B virus replication through flap endonuclease 1. Life Sciences. 257. 118089–118089. 8 indexed citations
5.
Guan, Na‐Na, et al.. (2019). Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization. Molecular Therapy — Nucleic Acids. 17. 164–174. 65 indexed citations
6.
Chen, Xing, Jia Qu, & Jun Yin. (2018). TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction. Frontiers in Genetics. 9. 234–234. 24 indexed citations
7.
Yu, Bin, Shan Li, Wenying Qiu, et al.. (2018). Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction. BMC Genomics. 19(1). 478–478. 54 indexed citations
8.
Guan, Na‐Na, Yazhou Sun, Zhong Ming, Jianqiang Li, & Xing Chen. (2018). Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction. Frontiers in Pharmacology. 9. 1152–1152. 44 indexed citations
9.
10.
Qu, Jia, Xing Chen, Yazhou Sun, et al.. (2018). In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm. Molecular Therapy — Nucleic Acids. 14. 274–286. 46 indexed citations
11.
Zhang, Lin, Xing Chen, Na‐Na Guan, Hui Liu, & Jianqiang Li. (2018). A Hybrid Interpolation Weighted Collaborative Filtering Method for Anti-cancer Drug Response Prediction. Frontiers in Pharmacology. 9. 1017–1017. 33 indexed citations
12.
Wang, Lei, Zhu‐Hong You, Shixiong Xia, et al.. (2017). Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier. Journal of Theoretical Biology. 418. 105–110. 33 indexed citations
13.
Li, Zhengwei, Zhu‐Hong You, Xiao Li, et al.. (2017). In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Scientific Reports. 7(1). 11174–11174. 70 indexed citations
14.
An, Jiyong, et al.. (2016). Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information. Molecular BioSystems. 12(12). 3702–3710. 9 indexed citations
15.
Chen, Xing, Biao Ren, Ming Chen, et al.. (2016). NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning. PLoS Computational Biology. 12(7). e1004975–e1004975. 236 indexed citations
16.
Sun, Xiaoqiang, Jiajun Zhang, Qi Zhao, et al.. (2016). Stochastic modeling suggests that noise reduces differentiation efficiency by inducing a heterogeneous drug response in glioma differentiation therapy. BMC Systems Biology. 10(1). 73–73. 10 indexed citations
17.
Huang, Yu‐An, Zhu‐Hong You, Xing Chen, & Guiying Yan. (2016). Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition. BMC Systems Biology. 10(S4). 120–120. 18 indexed citations
18.
Chen, Xing. (2015). KATZLDA: KATZ measure for the lncRNA-disease association prediction. Scientific Reports. 5(1). 16840–16840. 198 indexed citations
19.
Zhou, Jianya, Xi Chen, Jing Zhao, et al.. (2014). MicroRNA-34a overcomes HGF-mediated gefitinib resistance in EGFR mutant lung cancer cells partly by targeting MET. Cancer Letters. 351(2). 265–271. 75 indexed citations
20.
Chen, Xing, Ming-Xi Liu, Qinghua Cui, & Guiying Yan. (2012). Prediction of Disease-Related Interactions between MicroRNAs and Environmental Factors Based on a Semi-Supervised Classifier. PLoS ONE. 7(8). e43425–e43425. 54 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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