Xiaotong Yu

1.3k total citations · 1 hit paper
34 papers, 955 citations indexed

About

Xiaotong Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Xiaotong Yu has authored 34 papers receiving a total of 955 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 4 papers in Computational Mechanics. Recurrent topics in Xiaotong Yu's work include Text and Document Classification Technologies (5 papers), Sparse and Compressive Sensing Techniques (3 papers) and Photovoltaic System Optimization Techniques (3 papers). Xiaotong Yu is often cited by papers focused on Text and Document Classification Technologies (5 papers), Sparse and Compressive Sensing Techniques (3 papers) and Photovoltaic System Optimization Techniques (3 papers). Xiaotong Yu collaborates with scholars based in China, United Kingdom and Taiwan. Xiaotong Yu's co-authors include Xudong Zhao, Xingxing Zhang, Jihuan Xu, Stefán Thor Smith, Saiji Fu, Yingjie Tian, Jingchun Shen, Abid Hussain, Muhammad Moazzam and Yuanyu Huang and has published in prestigious journals such as Nature Immunology, Renewable and Sustainable Energy Reviews and Applied Energy.

In The Last Decade

Xiaotong Yu

31 papers receiving 932 citations

Hit Papers

The landscape of nanoparticle-based siRNA delivery and th... 2024 2026 2025 2024 25 50 75
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The landscape of nanoparticle-based siRNA delivery and therapeutic development
    2024 89 citations Muhammad Moazzam, Jun Zhang et al. Molecular Therapy profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaotong Yu China 13 525 252 240 192 98 34 955
Fei Cao China 23 592 1.1× 575 2.3× 305 1.3× 194 1.0× 44 0.4× 108 1.8k
Sachin Untawale India 12 229 0.4× 119 0.5× 71 0.3× 116 0.6× 23 0.2× 24 587
Shan Juan Xie China 19 139 0.3× 129 0.5× 79 0.3× 360 1.9× 32 0.3× 40 1.3k
Mohamed Issa Egypt 11 126 0.2× 54 0.2× 241 1.0× 116 0.6× 71 0.7× 23 569
Hai Lu China 16 207 0.4× 172 0.7× 57 0.2× 491 2.6× 16 0.2× 51 807
J.M. Herrero Spain 17 36 0.1× 97 0.4× 163 0.7× 216 1.1× 46 0.5× 68 1.2k

Countries citing papers authored by Xiaotong Yu

Since Specialization
Citations

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

Fields of papers citing papers by Xiaotong Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaotong Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaotong Yu. A scholar is included among the top collaborators of Xiaotong 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 Xiaotong Yu. Xiaotong 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.
Kong, Wan Ting, et al.. (2026). Temporal and spatial atlas of eosinophil specialization across tissues. Nature Immunology. 27(2). 364–375.
2.
Yu, Xiaotong, et al.. (2025). Open-vocabulary Object Detection Survey. Procedia Computer Science. 266. 62–71.
3.
Yu, Xiaotong, Chen Huang, Yan Song, et al.. (2025). Research progress and perspectives on the application of tyramide signal amplification-based multiplex immunohistochemistry/immunofluorescence: a bibliometrics analysis. Frontiers in Oncology. 14. 1473414–1473414. 1 indexed citations
4.
Yu, Xiaotong, et al.. (2025). Sample selection for noisy partial label learning with interactive contrastive learning. Pattern Recognition. 166. 111681–111681. 1 indexed citations
5.
Zhu, Yunpeng, et al.. (2024). Effects of the yaw error and the fault conditions on the dynamic characteristics of the 15 MW offshore semi-submersible wind turbine. Ocean Engineering. 300. 117440–117440. 12 indexed citations
6.
7.
Luo, Geping, Chaofan Li, Yu Zhang, et al.. (2024). Grazing weakens the carbon sequestration capacity of dry temperate grassland ecosystems in Central Asia. CATENA. 248. 108608–108608. 3 indexed citations
8.
Gao, Yuan, et al.. (2024). Numerical study on frontal polymerization subjected to buoyancy-induced convection: Front acceleration and instability. International Journal of Heat and Mass Transfer. 240. 126622–126622. 1 indexed citations
9.
Zhang, Xiaoliu, Yu Fu, Xiaotong Yu, et al.. (2024). Ergothioneine Ameliorates Liver Fibrosis by Inhibiting Glycerophospholipids Metabolism and TGF‐β/Smads Signaling Pathway: Based on Metabonomics and Network Pharmacology. Journal of Applied Toxicology. 45(3). 514–530. 3 indexed citations
10.
Moazzam, Muhammad, et al.. (2024). The landscape of nanoparticle-based siRNA delivery and therapeutic development. Molecular Therapy. 32(2). 284–312. 89 indexed citations breakdown →
11.
Yu, Xiaotong, et al.. (2023). Self-distillation and self-supervision for partial label learning. Pattern Recognition. 146. 110016–110016. 8 indexed citations
12.
Yu, Xiaotong, et al.. (2023). Multi-view prototype-based disambiguation for partial label learning. Pattern Recognition. 141. 109625–109625. 6 indexed citations
13.
Tian, Yingjie, et al.. (2023). Causal multi-label learning for image classification. Neural Networks. 167. 626–637. 7 indexed citations
14.
Tian, Yingjie, Xiaotong Yu, & Saiji Fu. (2023). Partial label learning: Taxonomy, analysis and outlook. Neural Networks. 161. 708–734. 24 indexed citations
15.
Tang, Jingjing, et al.. (2023). Multi-view cost-sensitive kernel learning for imbalanced classification problem. Neurocomputing. 552. 126562–126562. 19 indexed citations
16.
Yu, Xiaotong, Xiangyi Li, Huaming Wu, & Yang Bai. (2020). DS-NLCsiNet: Exploiting Non-Local Neural Networks for Massive MIMO CSI Feedback. IEEE Communications Letters. 24(12). 2790–2794. 29 indexed citations
17.
Fang, Shih‐Hau, et al.. (2019). CityTracker: Citywide Individual and Crowd Trajectory Analysis Using Hidden Markov Model. IEEE Sensors Journal. 19(17). 7693–7701. 15 indexed citations
18.
19.
Yu, Xiaotong, et al.. (2018). DNN-based Approach to Detect and Classify Pathological Voice. 5238–5241. 11 indexed citations
20.
Zhang, Xingxing, Xudong Zhao, Jingchun Shen, Jihuan Xu, & Xiaotong Yu. (2013). Dynamic performance of a novel solar photovoltaic/loop-heat-pipe heat pump system. Applied Energy. 114. 335–352. 101 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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