Ying‐Chuan Chen

1.3k total citations
64 papers, 1.0k citations indexed

About

Ying‐Chuan Chen is a scholar working on Neurology, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Ying‐Chuan Chen has authored 64 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Neurology, 21 papers in Cellular and Molecular Neuroscience and 6 papers in Neurology. Recurrent topics in Ying‐Chuan Chen's work include Neurological disorders and treatments (33 papers), Parkinson's Disease Mechanisms and Treatments (20 papers) and Neuroscience and Neuropharmacology Research (9 papers). Ying‐Chuan Chen is often cited by papers focused on Neurological disorders and treatments (33 papers), Parkinson's Disease Mechanisms and Treatments (20 papers) and Neuroscience and Neuropharmacology Research (9 papers). Ying‐Chuan Chen collaborates with scholars based in China, Taiwan and United States. Ying‐Chuan Chen's co-authors include Preston T. Snee, Rahul Thakar, Chung‐Che Chou, Guanyu Zhu, Jianguo Zhang, Tingting Du, Defeng Liu, Yin Jiang, Lin Shi and Yuye Liu and has published in prestigious journals such as Journal of the American Chemical Society, Nano Letters and PLoS ONE.

In The Last Decade

Ying‐Chuan Chen

60 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ying‐Chuan Chen China 18 330 243 223 203 170 64 1.0k
Eric Kim United States 17 199 0.6× 395 1.6× 121 0.5× 278 1.4× 132 0.8× 22 1.1k
Diane Chan United States 15 445 1.3× 275 1.1× 87 0.4× 340 1.7× 20 0.1× 35 1.2k
Jiawei Wu China 8 139 0.4× 51 0.2× 33 0.1× 108 0.5× 179 1.1× 19 579
Gaia Rizzo Italy 22 114 0.3× 250 1.0× 96 0.4× 421 2.1× 8 0.0× 86 1.8k
Cang Chen China 17 118 0.4× 143 0.6× 79 0.4× 295 1.5× 6 0.0× 51 824
Christopher C. Matthews United States 10 94 0.3× 145 0.6× 141 0.6× 202 1.0× 9 0.1× 21 629
Shohei Matsumoto Japan 17 53 0.2× 114 0.5× 157 0.7× 285 1.4× 9 0.1× 57 893

Countries citing papers authored by Ying‐Chuan Chen

Since Specialization
Citations

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

Fields of papers citing papers by Ying‐Chuan Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ying‐Chuan Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Ying‐Chuan Chen. A scholar is included among the top collaborators of Ying‐Chuan 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 Ying‐Chuan Chen. Ying‐Chuan 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.
Chen, Ying‐Chuan, Chih‐Yang Hu, Yu‐Lung Lin, et al.. (2025). Long-term epidemiological insights into rickets: a nationwide population-based retrospective study. Clinical and Experimental Pediatrics. 68(11). 879–891.
2.
Liu, Pei‐Yao, Chih‐Yuan Chen, Yu‐Lung Lin, et al.. (2023). RNF128 regulates neutrophil infiltration and myeloperoxidase functions to prevent acute lung injury. Cell Death and Disease. 14(6). 21 indexed citations
3.
4.
Chien, Wu‐Chien, Chi‐Hsiang Chung, Ying‐Chuan Chen, et al.. (2023). An enigma of hypothyroidism and hyponatremia coexistence: a nationwide population-based retrospective study. BMC Public Health. 23(1). 1889–1889. 3 indexed citations
5.
Chen, Ying‐Chuan, Guanyu Zhu, Defeng Liu, et al.. (2022). Seed-Based Connectivity Prediction of Initial Outcome of Subthalamic Nuclei Deep Brain Stimulation. Neurotherapeutics. 19(2). 608–615. 10 indexed citations
6.
Du, Tingting, Le Wang, Weijin Liu, et al.. (2021). Biomarkers and the Role of α-Synuclein in Parkinson’s Disease. Frontiers in Aging Neuroscience. 13. 645996–645996. 29 indexed citations
7.
Liu, Pei‐Yao, Cheng-Cheung Chen, Wen‐Chiuan Tsai, et al.. (2021). E3 ubiquitin ligase Grail promotes hepatic steatosis through Sirt1 inhibition. Cell Death and Disease. 12(4). 323–323. 23 indexed citations
8.
Chen, Ying‐Chuan, Guanyu Zhu, Defeng Liu, et al.. (2020). The morphology of thalamic subnuclei in Parkinson's disease and the effects of machine learning on disease diagnosis and clinical evaluation. Journal of the Neurological Sciences. 411. 116721–116721. 24 indexed citations
9.
Zhu, Guanyu, Ying‐Chuan Chen, Tingting Du, et al.. (2019). The Accuracy and Feasibility of Robotic Assisted Lead Implantation in Nonhuman Primates. Neuromodulation Technology at the Neural Interface. 22(4). 441–450. 11 indexed citations
10.
Liu, Defeng, Ying‐Chuan Chen, Guanyu Zhu, et al.. (2019). Effects of anterior thalamic nuclei stimulation on gene expression in a rat model of temporal lobe epilepsy. Acta Neurologica Belgica. 120(6). 1361–1370. 7 indexed citations
11.
Shih, Chih‐Chin, Pei‐Yao Liu, Mei‐Hui Liao, et al.. (2018). Macrophage expression of E3 ubiquitin ligase Grail protects mice from lipopolysaccharide-induced hyperinflammation and organ injury. PLoS ONE. 13(12). e0208279–e0208279. 9 indexed citations
12.
Liu, Peiyao, et al.. (2018). Grail is involved in adipocyte differentiation and diet-induced obesity. Cell Death and Disease. 9(5). 525–525. 14 indexed citations
13.
Zhu, Guanyu, Xinyi Geng, Ying‐Chuan Chen, et al.. (2018). Characteristics of Globus Pallidus Internus Local Field Potentials in Hyperkinetic Disease. Frontiers in Neurology. 9. 934–934. 19 indexed citations
14.
Liu, Yuye, Guanyu Zhu, Yin Jiang, et al.. (2018). Comparison of Short-Term Stimulation of the Globus Pallidus Interna and Subthalamic Nucleus for Treatment of Primary Dystonia. World Neurosurgery. 123. e211–e217. 16 indexed citations
15.
Du, Tingting, Ying‐Chuan Chen, Yongquan Lu, et al.. (2018). Subthalamic nucleus deep brain stimulation protects neurons by activating autophagy via PP2A inactivation in a rat model of Parkinson's disease. Experimental Neurology. 306. 232–242. 21 indexed citations
16.
Shi, Lin, et al.. (2017). Ultrahigh-Magnitude Brain Magnetic Resonance Imaging Scan on Rhesus Monkeys With Implanted Deep Brain Stimulation Hardware. Neuromodulation Technology at the Neural Interface. 21(2). 168–175.
17.
Zhu, Guanyu, Ying‐Chuan Chen, Lin Shi, et al.. (2016). Error Analysis and Some Suggestions on Animal Stereotactic Experiment from Inaccuracy of Rhesus Macaques Atlas. Chinese Medical Journal. 129(13). 1621–1624. 4 indexed citations
18.
Chou, Chung‐Che, et al.. (2014). Steel braced frames with dual-core SCBs and sandwiched BRBs: Mechanics, modeling and seismic demands. Engineering Structures. 72. 26–40. 106 indexed citations
19.
Chan, James Yi‐Hsin, Ying‐Chuan Chen, Shuting Liu, et al.. (2014). Characterization of a new mouse p53 variant: loss-of-function and gain-of-function. Journal of Biomedical Science. 21(1). 40–40. 3 indexed citations
20.
Chou, Chung‐Che & Ying‐Chuan Chen. (2013). Development of Steel Dual‐Core Self‐Centering Braces: Quasi‐Static Cyclic Tests and Finite Element Analyses. Earthquake Spectra. 31(1). 247–272. 64 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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