Xiaoyue Feng

1.8k total citations · 1 hit paper
46 papers, 1.1k citations indexed

About

Xiaoyue Feng is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Xiaoyue Feng has authored 46 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 15 papers in Molecular Biology and 8 papers in Information Systems. Recurrent topics in Xiaoyue Feng's work include Topic Modeling (15 papers), Advanced Graph Neural Networks (10 papers) and Text and Document Classification Technologies (6 papers). Xiaoyue Feng is often cited by papers focused on Topic Modeling (15 papers), Advanced Graph Neural Networks (10 papers) and Text and Document Classification Technologies (6 papers). Xiaoyue Feng collaborates with scholars based in China, Italy and United States. Xiaoyue Feng's co-authors include Yanchun Liang, Renchu Guan, Dong Xu, Donghui Wang, Fausto Giunchiglia, Mingyang Jiang, Zhili Pei, Yu Xue, Xiaojing Fan and Lan Huang and has published in prestigious journals such as Bioinformatics, Nature Methods and International Journal of Molecular Sciences.

In The Last Decade

Xiaoyue Feng

41 papers receiving 1.1k citations

Hit Papers

A content-based recommender system for computer science p... 2018 2026 2020 2023 2018 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaoyue Feng China 16 618 303 166 148 82 46 1.1k
Saman Forouzandeh Australia 15 483 0.8× 198 0.7× 188 1.1× 166 1.1× 95 1.2× 24 917
Pramod Kumar Singh India 18 573 0.9× 215 0.7× 182 1.1× 63 0.4× 79 1.0× 62 948
Francisco Charte Spain 15 676 1.1× 235 0.8× 176 1.1× 83 0.6× 41 0.5× 41 990
Yuhai Zhao China 16 421 0.7× 141 0.5× 152 0.9× 134 0.9× 85 1.0× 95 790
Shengli Wu United Kingdom 18 527 0.9× 381 1.3× 212 1.3× 85 0.6× 108 1.3× 103 1.1k
Xiao Luo China 23 784 1.3× 203 0.7× 471 2.8× 165 1.1× 58 0.7× 129 1.6k
Alper Kürşat Uysal Türkiye 17 1.1k 1.9× 616 2.0× 187 1.1× 77 0.5× 92 1.1× 29 1.8k
Peter Andreae New Zealand 21 792 1.3× 216 0.7× 163 1.0× 112 0.8× 80 1.0× 92 1.5k
Shu Zhao China 20 649 1.1× 306 1.0× 275 1.7× 42 0.3× 70 0.9× 110 1.1k

Countries citing papers authored by Xiaoyue Feng

Since Specialization
Citations

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

Fields of papers citing papers by Xiaoyue Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaoyue Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoyue Feng. A scholar is included among the top collaborators of Xiaoyue Feng 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 Xiaoyue Feng. Xiaoyue Feng 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, Zhikang, Liang Chen, Zhi Li, et al.. (2025). High-parameter spatial multi-omics through histology-anchored integration. Nature Methods. 23(2). 373–386.
2.
Pang, Wei, et al.. (2025). Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 39(23). 24696–24704.
3.
Li, Ximing, Lan Huang, Fausto Giunchiglia, et al.. (2024). Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training. ACM Transactions on Knowledge Discovery from Data. 18(9). 1–30.
4.
Feng, Xiaoyue, Dan Wang, Runhui Liu, et al.. (2024). Long‐term clinical outcomes of extracorporeal shockwave lithotripsy and endoscopic retrograde cholangiopancreatography for pancreatic duct stone treatment in patients with chronic pancreatitis. Alimentary Pharmacology & Therapeutics. 60(8). 1110–1121. 1 indexed citations
5.
Chen, Chunyan, Jing Feng, Shuping Zhou, et al.. (2023). Muc2 mucin o-glycosylation interacts with enteropathogenic Escherichia coli to influence the development of ulcerative colitis based on the NF-kB signaling pathway. Journal of Translational Medicine. 21(1). 793–793. 20 indexed citations
6.
Feng, Xiaoyue, et al.. (2023). Multi-Agent Deep Reinforcement Learning for Efficient Computation Offloading in Mobile Edge Computing. Computers, materials & continua/Computers, materials & continua (Print). 76(3). 3585–3603. 1 indexed citations
7.
Guan, Renchu, Xiao Yu, Qingyu Chen, et al.. (2023). Deep Learning Model for Coronary Angiography. Journal of Cardiovascular Translational Research. 16(4). 896–904. 4 indexed citations
9.
Guan, Renchu, Yanchun Liang, Zhongjun Shao, et al.. (2022). Discovering trends and hotspots of biosafety and biosecurity research via machine learning. Briefings in Bioinformatics. 23(5). 2 indexed citations
10.
Huang, Lan, et al.. (2021). COVID-19 Knowledge Graph for Drug and Vaccine Development. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 32. 328–333. 3 indexed citations
11.
Guan, Renchu, et al.. (2021). Deep Attention Diffusion Graph Neural Networks for Text Classification. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 8142–8152. 36 indexed citations
12.
Liang, Yanchun, et al.. (2020). BioNMT: A Biomedical Neural Machine Translation System. International Journal of Computers Communications & Control. 15(6). 2 indexed citations
13.
Feng, Xiaoyue, Hao Zhang, Yi Zhu, et al.. (2019). The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study. Journal of Medical Internet Research. 21(5). e12957–e12957. 43 indexed citations
14.
Zhang, Hao, Renchu Guan, Fengfeng Zhou, et al.. (2019). Deep Residual Convolutional Neural Network for Protein-Protein Interaction Extraction. IEEE Access. 7. 89354–89365. 21 indexed citations
15.
Guan, Renchu, et al.. (2019). Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts. International Journal of Biological Sciences. 15(10). 2065–2074. 16 indexed citations
16.
Lin, Xixun, Yanchun Liang, Fausto Giunchiglia, Xiaoyue Feng, & Renchu Guan. (2018). Relation path embedding in knowledge graphs. Neural Computing and Applications. 31(9). 5629–5639. 23 indexed citations
17.
Zhang, Hao, Mary Qu Yang, Xiaoyue Feng, et al.. (2017). Protein-Protein Interaction Extraction Using Attention-Based Convolution Neural Networks. 770–771. 1 indexed citations
18.
Feng, Xiaoyue, Yanchun Liang, Xiaohu Shi, et al.. (2017). Overfitting Reduction of Text Classification Based on AdaBELM. Entropy. 19(7). 330–330. 24 indexed citations
19.
Song, Yang, Yanhong Shi, Changqin Liu, et al.. (2015). Severe Henoch-Schönlein purpura with infliximab for ulcerative colitis. World Journal of Gastroenterology. 21(19). 6082–6087. 14 indexed citations
20.
Wang, Yan, Xiaoyue Feng, Yanxin Huang, et al.. (2006). A novel quantum swarm evolutionary algorithm and its applications. Neurocomputing. 70(4-6). 633–640. 118 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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