Shuo Wang

6.4k total citations
342 papers, 4.1k citations indexed

About

Shuo Wang is a scholar working on Neurology, Pulmonary and Respiratory Medicine and Epidemiology. According to data from OpenAlex, Shuo Wang has authored 342 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 214 papers in Neurology, 63 papers in Pulmonary and Respiratory Medicine and 62 papers in Epidemiology. Recurrent topics in Shuo Wang's work include Intracranial Aneurysms: Treatment and Complications (144 papers), Vascular Malformations Diagnosis and Treatment (85 papers) and Moyamoya disease diagnosis and treatment (60 papers). Shuo Wang is often cited by papers focused on Intracranial Aneurysms: Treatment and Complications (144 papers), Vascular Malformations Diagnosis and Treatment (85 papers) and Moyamoya disease diagnosis and treatment (60 papers). Shuo Wang collaborates with scholars based in China, United States and Australia. Shuo Wang's co-authors include Jizong Zhao, Yong Cao, Rong Wang, Yuanli Zhao, Yan Zhang, Jun Wu, Meng Zhao, Xiaofeng Deng, Dong Zhang and Dong Zhang and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Shuo Wang

319 papers receiving 4.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shuo Wang China 31 2.0k 872 729 695 611 342 4.1k
Nils Peters Germany 36 1.6k 0.8× 678 0.8× 616 0.8× 802 1.2× 302 0.5× 107 3.9k
Shinji Nagahiro Japan 42 2.7k 1.4× 653 0.7× 1.3k 1.7× 877 1.3× 214 0.4× 296 6.0k
Koji Iihara Japan 38 2.5k 1.3× 754 0.9× 1.9k 2.6× 947 1.4× 505 0.8× 328 6.3k
Joab Chapman Israel 48 1.8k 0.9× 2.0k 2.3× 401 0.6× 2.3k 3.3× 598 1.0× 288 7.8k
Thibault Moreau France 31 1.4k 0.7× 818 0.9× 372 0.5× 848 1.2× 365 0.6× 98 5.7k
Panayiotis Mitsias United States 29 908 0.5× 199 0.2× 592 0.8× 323 0.5× 352 0.6× 100 2.7k
Kentarō Hayashi Japan 29 932 0.5× 426 0.5× 667 0.9× 402 0.6× 233 0.4× 221 2.7k
Motoji Sawabe Japan 42 1.2k 0.6× 300 0.3× 1.1k 1.5× 1.5k 2.2× 224 0.4× 232 6.7k
Monika Baier United States 34 673 0.3× 729 0.8× 720 1.0× 562 0.8× 189 0.3× 60 4.5k
Robert D. Henderson Australia 43 3.0k 1.5× 215 0.2× 1.1k 1.5× 727 1.0× 377 0.6× 228 6.8k

Countries citing papers authored by Shuo Wang

Since Specialization
Citations

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

Fields of papers citing papers by Shuo Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shuo Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Shuo Wang. A scholar is included among the top collaborators of Shuo Wang 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 Shuo Wang. Shuo Wang 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.
Wang, Shuo, Zhenhua Wang, Yungang Bai, Jianghui Zhang, & Fang Zheng. (2025). Optimizing root zone environment to enhance technology stability of “dry sowing and wet emergence” in cotton fields of southern Xinjiang, China. Agricultural Water Management. 321. 109895–109895.
2.
Pan, Jonathan A., Shuo Wang, Jeremy Slivnick, et al.. (2025). Deep learning to predict myocardial scar burden and uncertainty quantification. Journal of Cardiovascular Magnetic Resonance. 27. 101530–101530. 1 indexed citations
3.
Hill, Jon, et al.. (2025). Human three-dimensional engineered muscle tissue characterization and intramyocellular lipid modeling. Journal of Tissue Engineering. 16. 1798873014–1798873014.
4.
Wang, Shuo, et al.. (2024). Distribution of polycyclic aromatic hydrocarbons and carcinogenic risk assessment in street-barbecued foods in China. Journal of Food Composition and Analysis. 137. 106890–106890. 1 indexed citations
5.
Lin, Songyi, et al.. (2024). κ-Carrageenan improves the gelation capacity of Antarctic krill myofibrillar proteins through covalent and noncovalent interactions. Food Hydrocolloids. 155. 110220–110220. 23 indexed citations
6.
Wang, Shuo, Ying Liu, Meng Zhou, et al.. (2024). Based on Eudragit® encapsulated ionic polymer IR775@nido-carborane strategy: release, bioactivity and tumor cell imaging studies in simulated gastrointestinal environment. Macromolecular Research. 32(6). 525–540. 7 indexed citations
7.
Yang, Yi, et al.. (2024). Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography. Radiology Artificial Intelligence. 7(1). e240017–e240017. 1 indexed citations
8.
Li, Xiaoliang, et al.. (2024). Measurements of 131I activity in thyroid of workers at the place of radioiodine therapy in 38 hospitals in China. Journal of Radiation Research. 65(4). 459–466.
9.
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11.
Liu, Qingyuan, Kaiwen Wang, Yanan Zhang, et al.. (2023). Model based on single-nucleotide polymorphism to discriminate aspirin resistance patients. Stroke and Vascular Neurology. 9(3). 212–220. 4 indexed citations
12.
Huynh, Linda My, et al.. (2023). Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy. Journal of Clinical Medicine. 12(23). 7322–7322. 8 indexed citations
13.
Wang, Dandan, Ying Zhang, Shuo Wang, et al.. (2022). Efficacy and safety of Xuebijing injection for radiation pneumonitis: A meta-analysis. PLoS ONE. 17(6). e0268819–e0268819. 1 indexed citations
14.
Wang, Shuo, Haojun Yu, Yiqiu Zhang, et al.. (2022). An artificial intelligence-driven image quality assessment system for whole-body [18F]FDG PET/CT. European Journal of Nuclear Medicine and Molecular Imaging. 50(5). 1318–1328. 17 indexed citations
15.
Zhang, Qingzhen, Fa Lin, Qiheng He, et al.. (2022). Multimodal treatments of brain arteriovenous malformations: a comparison of microsurgical timings after endovascular embolization. Annals of Translational Medicine. 10(13). 732–732. 5 indexed citations
16.
Wang, Min, Shanchen Pang, Sibo Qiao, et al.. (2021). DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT. IEEE Transactions on Industrial Informatics. 18(5). 3406–3415. 3 indexed citations
17.
Zhang, Dong, et al.. (2021). Differentially Expressed Circular RNA Profile in an Intracranial Aneurysm Group Compared with a Healthy Control Group. Disease Markers. 2021. 1–8. 11 indexed citations
18.
Ye, Xun, Xingju Liu, Xiaofeng Deng, et al.. (2015). [The clinical application of retractorless surgical technique in neurosurgical operation].. PubMed. 95(25). 1976–9. 1 indexed citations
19.
20.
Wang, Shuo, et al.. (2010). [Ninety cases of postoperative complications in elderly patients after surgical removal of meningiomas].. PubMed. 90(5). 298–300. 1 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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