Yue Yu

1.1k total citations
50 papers, 711 citations indexed

About

Yue Yu is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Yue Yu has authored 50 papers receiving a total of 711 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cognitive Neuroscience, 10 papers in Computer Vision and Pattern Recognition and 8 papers in Artificial Intelligence. Recurrent topics in Yue Yu's work include Functional Brain Connectivity Studies (11 papers), Mental Health Research Topics (6 papers) and Image and Signal Denoising Methods (4 papers). Yue Yu is often cited by papers focused on Functional Brain Connectivity Studies (11 papers), Mental Health Research Topics (6 papers) and Image and Signal Denoising Methods (4 papers). Yue Yu collaborates with scholars based in China, Singapore and United States. Yue Yu's co-authors include Jun Wang, Huafu Chen, Wei Sheng, Zongling He, Qian Cui, Fengmei Lu, Yuyan Chen, Kaibo Shi, Wei Luo and Kun She and has published in prestigious journals such as Cerebral Cortex, IEEE Access and Journal of Affective Disorders.

In The Last Decade

Yue Yu

39 papers receiving 703 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yue Yu China 16 275 134 89 88 81 50 711
David Cuesta–Frau Spain 19 351 1.3× 53 0.4× 61 0.7× 26 0.3× 136 1.7× 72 1.1k
Bilal Fadlallah United States 6 234 0.9× 27 0.2× 41 0.5× 70 0.8× 82 1.0× 13 912
Matthias Arnold Germany 14 1.2k 4.5× 78 0.6× 56 0.6× 55 0.6× 172 2.1× 41 1.6k
Régine Le Bouquin Jeannès France 18 556 2.0× 30 0.2× 70 0.8× 35 0.4× 180 2.2× 109 1.4k
Mosabber Uddin Ahmed Bangladesh 14 235 0.9× 37 0.3× 33 0.4× 25 0.3× 112 1.4× 32 855
Junjie Chen China 17 557 2.0× 145 1.1× 68 0.8× 110 1.3× 105 1.3× 81 933
Xiangwei Zheng China 21 539 2.0× 449 3.4× 140 1.6× 48 0.5× 416 5.1× 136 1.6k
Sofiane Ramdani France 13 221 0.8× 35 0.3× 46 0.5× 12 0.1× 51 0.6× 39 984
Brandon Westover United States 14 360 1.3× 27 0.2× 62 0.7× 41 0.5× 475 5.9× 23 1.4k
Fabio La Foresta Italy 18 743 2.7× 29 0.2× 17 0.2× 38 0.4× 89 1.1× 58 1.3k

Countries citing papers authored by Yue Yu

Since Specialization
Citations

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

Fields of papers citing papers by Yue Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yue Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Yue Yu. A scholar is included among the top collaborators of Yue Yu 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 Yue Yu. Yue Yu 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, Yueheng, Yue Yu, Lu Jiang, et al.. (2026). Disease-specific network pattern of perinatal depression revealed by Common Orthogonal Basis Extraction. Brain Research Bulletin. 234. 111719–111719.
2.
Wang, Lin, et al.. (2025). A Personalized Closed-Loop Brain Stimulation Protocol for Difficulty Falling Asleep. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 33. 2368–2380.
3.
Li, Qianqian, Qi Li, Gu Zhang, et al.. (2025). Disrupted interhemispheric functional and structural connectivity in patients with major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 139. 111374–111374.
4.
Zhang, Han, Lin Gui, Zhuo Zhang, et al.. (2025). COPR: Continual Human Preference Learning via Optimal Policy Regularization. 5377–5398. 1 indexed citations
5.
Dall’erba, Sandy, et al.. (2024). Identifying the key atmospheric and economic drivers of global carbon monoxide emission transfers. Economic Systems Research. 36(3). 404–421. 1 indexed citations
6.
Lu, Fengmei, Qian Cui, Wei Luo, et al.. (2023). Effects of rTMS Intervention on Functional Neuroimaging Activities in Adolescents with Major Depressive Disorder Measured Using Resting-State fMRI. Bioengineering. 10(12). 1374–1374. 6 indexed citations
7.
She, Kun, et al.. (2023). Enhancing Traceability Link Recovery with Fine-Grained Query Expansion Analysis. Information. 14(5). 270–270. 3 indexed citations
8.
Lu, Fengmei, Qian Cui, Yajing Pang, et al.. (2023). Shared and distinct patterns of dynamic functional connectivity variability of thalamo-cortical circuit in bipolar depression and major depressive disorder. Cerebral Cortex. 33(11). 6681–6692. 18 indexed citations
9.
Lu, Fengmei, Qian Cui, Yuyan Chen, et al.. (2022). Insular-associated causal network of structural covariance evaluating progressive gray matter changes in major depressive disorder. Cerebral Cortex. 33(3). 831–843. 27 indexed citations
10.
Fan, Lixian, Yue Yu, & Jingbo Yin. (2022). Impact of Sulphur Emission Control Areas on port state control’s inspection outcome. Maritime Policy & Management. 50(7). 908–923. 15 indexed citations
11.
12.
Yang, Haiyang, et al.. (2022). Sample-Efficient Deep Reinforcement Learning via Balance Sample. 890–895. 2 indexed citations
13.
Hayat, Shaukat, et al.. (2021). Entropy information‐based heterogeneous deep selective fused features using deep convolutional neural network for sketch recognition. IET Computer Vision. 15(3). 165–180. 6 indexed citations
14.
He, Zongling, Fengmei Lu, Wei Sheng, et al.. (2020). Abnormal functional connectivity as neural biological substrate of trait and state characteristics in major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 102. 109949–109949. 19 indexed citations
15.
Lu, Fengmei, Qian Cui, Liyuan Li, et al.. (2020). Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 100. 109889–109889. 73 indexed citations
16.
Zhang, Mingyang, Tong Li, Yue Yu, et al.. (2020). Urban Anomaly Analytics: Description, Detection, and Prediction. IEEE Transactions on Big Data. 8(3). 809–826. 64 indexed citations
17.
Zhang, Yang, et al.. (2019). Multi-reviewing pull-requests: An exploratory study on GitHub OSS projects. Information and Software Technology. 115. 1–4. 2 indexed citations
18.
Jiang, Guoqian, Yue Yu, Paul Kingsbury, & Nilay D. Shah. (2019). Augmenting Medical Device Evaluation Using a Reusable Unique Device Identifier Interoperability Solution Based on the OHDSI Common Data Model. Studies in health technology and informatics. 264. 1502–1503. 5 indexed citations
19.
He, Zongling, Fengmei Lu, Wei Sheng, et al.. (2019). Functional dysconnectivity within the emotion-regulating system is associated with affective symptoms in major depressive disorder: A resting-state fMRI study. Australian & New Zealand Journal of Psychiatry. 53(6). 528–539. 43 indexed citations
20.
He, Zongling, Wei Sheng, Fengmei Lu, et al.. (2018). Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 90. 177–185. 63 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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