Xiaojuan Guo

2.0k total citations
79 papers, 1.5k citations indexed

About

Xiaojuan Guo is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Psychiatry and Mental health. According to data from OpenAlex, Xiaojuan Guo has authored 79 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Cognitive Neuroscience, 22 papers in Radiology, Nuclear Medicine and Imaging and 17 papers in Psychiatry and Mental health. Recurrent topics in Xiaojuan Guo's work include Functional Brain Connectivity Studies (32 papers), Advanced Neuroimaging Techniques and Applications (19 papers) and Dementia and Cognitive Impairment Research (15 papers). Xiaojuan Guo is often cited by papers focused on Functional Brain Connectivity Studies (32 papers), Advanced Neuroimaging Techniques and Applications (19 papers) and Dementia and Cognitive Impairment Research (15 papers). Xiaojuan Guo collaborates with scholars based in China, United States and Japan. Xiaojuan Guo's co-authors include Kewei Chen, Jiacai Zhang, Yao Li, Li Yao, Xia Wu, Takesumi Yoshimura, Yoshihisa Fujino, Lele Xu, Ke Liu and Zhigang Qi and has published in prestigious journals such as PLoS ONE, Journal of Hazardous Materials and Journal of Agricultural and Food Chemistry.

In The Last Decade

Xiaojuan Guo

72 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaojuan Guo China 24 477 307 301 233 228 79 1.5k
Fang Fang China 21 161 0.3× 108 0.4× 143 0.5× 185 0.8× 172 0.8× 71 1.1k
Leonid Kopylev United States 19 790 1.7× 360 1.2× 185 0.6× 60 0.3× 157 0.7× 37 2.1k
Huijun Li China 18 139 0.3× 292 1.0× 108 0.4× 90 0.4× 314 1.4× 65 1.2k
Ya‐Nan Ou China 25 163 0.3× 835 2.7× 205 0.7× 826 3.5× 194 0.9× 104 2.6k
Duan Liu China 25 403 0.8× 230 0.7× 123 0.4× 155 0.7× 66 0.3× 81 1.8k
Lisa C. Silbert United States 31 301 0.6× 825 2.7× 479 1.6× 872 3.7× 91 0.4× 98 2.9k
Chunyan Lu China 24 147 0.3× 125 0.4× 158 0.5× 221 0.9× 166 0.7× 71 2.0k
Zan Wang China 23 670 1.4× 375 1.2× 302 1.0× 213 0.9× 39 0.2× 81 1.3k
Xueling Lu China 15 286 0.6× 658 2.1× 414 1.4× 683 2.9× 246 1.1× 25 1.5k
Nobuyoshi Ishii Japan 20 91 0.2× 588 1.9× 94 0.3× 288 1.2× 90 0.4× 57 1.9k

Countries citing papers authored by Xiaojuan Guo

Since Specialization
Citations

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

Fields of papers citing papers by Xiaojuan Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaojuan Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaojuan Guo. A scholar is included among the top collaborators of Xiaojuan Guo 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 Xiaojuan Guo. Xiaojuan Guo 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.
2.
Guo, Xiaojuan, et al.. (2024). Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network. Electronics. 13(20). 4054–4054. 1 indexed citations
3.
Li, Jingming, Qian Wang, Ke Li, Yao Li, & Xiaojuan Guo. (2023). Tracking Age‐Related Topological Changes in Individual Brain Morphological Networks Across the Human Lifespan. Journal of Magnetic Resonance Imaging. 59(5). 1841–1851. 2 indexed citations
4.
Li, Jingming, et al.. (2023). Estimating the connectional brain template based on multi-view networks with bi-channel graph neural network. Biomedical Signal Processing and Control. 89. 105798–105798.
5.
Lü, Na, Kewei Chen, Yao Li, et al.. (2020). Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks. Frontiers in Neurology. 10. 1346–1346. 60 indexed citations
6.
Wang, Yi, Sina Zhang, Xiaozai Xie, et al.. (2020). Association of TNFRSF12A Methylation With Prognosis in Hepatocellular Carcinoma With History of Alcohol Consumption. Frontiers in Genetics. 10. 1299–1299. 15 indexed citations
7.
Zhao, Xiaoyu, Yao Li, Kewei Chen, et al.. (2019). Changes in the Functional and Structural Default Mode Network Across the Adult Lifespan Based on Partial Least Squares. IEEE Access. 7. 82256–82265. 7 indexed citations
8.
Li, Guanying, et al.. (2017). Establishment of rapeseed (Brassica napus L.) cotyledon transient transformation system for gene function analysis.. Pakistan Journal of Botany. 49(6). 2227–2233. 2 indexed citations
9.
Wang, Shuangkun, et al.. (2017). Correlation between prefrontal-striatal pathway impairment and cognitive impairment in patients with leukoaraiosis. Medicine. 96(17). e6703–e6703. 13 indexed citations
10.
Liu, Ke, Kewei Chen, Yao Li, & Xiaojuan Guo. (2017). Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model. Frontiers in Human Neuroscience. 11. 33–33. 58 indexed citations
11.
Guo, Xiaojuan, et al.. (2015). Protective Effect of Folic Acid on Oxidative DNA Damage. Medicine. 94(45). e1872–e1872. 18 indexed citations
12.
Li, Ke, Xiaojuan Guo, Zhen Jin, et al.. (2015). Effect of Simulated Microgravity on Human Brain Gray Matter and White Matter – Evidence from MRI. PLoS ONE. 10(8). e0135835–e0135835. 47 indexed citations
13.
Chen, Kewei, Li Yao, Xia Wu, et al.. (2015). Independent Component Analysis-Based Identification of Covariance Patterns of Microstructural White Matter Damage in Alzheimer’s Disease. PLoS ONE. 10(3). e0119714–e0119714. 11 indexed citations
14.
Liu, Yunting, Xia Wu, Jiacai Zhang, et al.. (2015). Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI. Journal of Affective Disorders. 182. 8–17. 45 indexed citations
15.
Xu, Lele, Tingting Fan, Xia Wu, et al.. (2014). A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data. Frontiers in Computational Neuroscience. 8. 125–125. 14 indexed citations
16.
Liu, Li, et al.. (2013). Altered brain structure in Chinese dyslexic children. Neuropsychologia. 51(7). 1169–1176. 16 indexed citations
17.
Li, Han, Yan Zhang, Ning Wang, et al.. (2011). 代替指標としてCD8 + 細胞におけるローダミン123蓄積を用いる,In vivo塩酸セファランチンのP糖蛋白質調節作用の研究. BioMed Research International. 2011(1). 1–7. 108 indexed citations
18.
Ye, Xiaolei, et al.. (2010). Association between skin lesions and 8-OHdG among a chronic arsenic exposure population. China Environmental Science. 30(1). 99–103. 1 indexed citations
19.
Fujino, Yoshihisa, Xiaojuan Guo, Jun Liu, et al.. (2004). Chronic arsenic exposure and urinary 8-Hydroxy-2′-deoxyguanosine in an arsenic-affected area in Inner Mongolia, China. Journal of Exposure Science & Environmental Epidemiology. 15(2). 147–152. 45 indexed citations
20.
Guo, Xiaojuan, Yoshihisa Fujino, Kegong Wu, et al.. (2003). The Prevalence of Subjective Symptoms after Exposure to Arsenic in Drinking Water in Inner Mongolia, China. Journal of Epidemiology. 13(4). 211–215. 37 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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