Fei Zhu

2.9k total citations · 1 hit paper
97 papers, 1.7k citations indexed

About

Fei Zhu is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Fei Zhu has authored 97 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 34 papers in Molecular Biology and 19 papers in Computational Theory and Mathematics. Recurrent topics in Fei Zhu's work include Reinforcement Learning in Robotics (19 papers), Computational Drug Discovery Methods (15 papers) and Machine Learning in Bioinformatics (11 papers). Fei Zhu is often cited by papers focused on Reinforcement Learning in Robotics (19 papers), Computational Drug Discovery Methods (15 papers) and Machine Learning in Bioinformatics (11 papers). Fei Zhu collaborates with scholars based in China, United Kingdom and Canada. Fei Zhu's co-authors include Quan Liu, Bairong Shen, Fei Ye, Seth G. N. Grant, Noboru H. Komiyama, René Frank, Wanwipa Vongsangnak, Javier DeFelipe, Maksym V. Kopanitsa and Ruth Benavides‐Piccione and has published in prestigious journals such as Nature Communications, Neuron and Bioinformatics.

In The Last Decade

Fei Zhu

87 papers receiving 1.7k citations

Hit Papers

Electrocardiogram generation with a bidirectional LSTM-CN... 2019 2026 2021 2023 2019 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fei Zhu China 18 621 371 296 221 182 97 1.7k
Yongqing Zhang China 26 941 1.5× 183 0.5× 111 0.4× 280 1.3× 62 0.3× 169 2.5k
Yuxing Dai China 30 412 0.7× 266 0.7× 212 0.7× 88 0.4× 33 0.2× 209 3.2k
Xueying Zhang China 23 273 0.4× 611 1.6× 288 1.0× 229 1.0× 38 0.2× 195 2.2k
Xiaowei Zhang China 26 352 0.6× 260 0.7× 155 0.5× 773 3.5× 173 1.0× 103 2.1k
Weigang Cui China 16 380 0.6× 129 0.3× 150 0.5× 853 3.9× 146 0.8× 37 1.5k
Xiang Wu China 23 780 1.3× 283 0.8× 272 0.9× 41 0.2× 41 0.2× 109 2.8k
Leonardo Franco Spain 24 230 0.4× 688 1.9× 223 0.8× 461 2.1× 42 0.2× 79 2.1k
Lei Cai China 28 1.9k 3.0× 161 0.4× 85 0.3× 91 0.4× 444 2.4× 111 3.4k
Rubén Armañanzas Spain 17 755 1.2× 308 0.8× 74 0.3× 141 0.6× 28 0.2× 37 1.5k
Sohan Seth United States 18 174 0.3× 521 1.4× 105 0.4× 382 1.7× 83 0.5× 72 1.9k

Countries citing papers authored by Fei Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Fei Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fei Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Fei Zhu. A scholar is included among the top collaborators of Fei Zhu 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 Fei Zhu. Fei Zhu 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.
Zhu, Fei, et al.. (2025). LkaM-PTM: Predicting PTM sites through multimodal protein features from capturing cross-field information. Artificial Intelligence in Medicine. 171. 103297–103297.
2.
Zhu, Fei, et al.. (2024). ExSelfRL: An exploration-inspired self-supervised reinforcement learning approach to molecular generation. Expert Systems with Applications. 260. 125410–125410. 2 indexed citations
3.
Zhu, Fei, et al.. (2024). Reconstruction of dynamic protein–protein interaction network via graph convolutional network. Expert Systems with Applications. 259. 125140–125140. 1 indexed citations
4.
Liu, Quan, et al.. (2023). Hierarchical reinforcement learning with adaptive scheduling for robot control. Engineering Applications of Artificial Intelligence. 126. 107130–107130. 12 indexed citations
5.
Zhu, Fei, et al.. (2023). Seek for commonalities: Shared features extraction for multi-task reinforcement learning via adversarial training. Expert Systems with Applications. 224. 119975–119975. 4 indexed citations
6.
Li, Fanzhang, et al.. (2023). Taking complementary advantages: Improving exploration via double self-imitation learning in procedurally-generated environments. Expert Systems with Applications. 238. 122145–122145. 5 indexed citations
7.
Zhu, Fei, et al.. (2023). Learning fair representations for accuracy parity. Engineering Applications of Artificial Intelligence. 119. 105819–105819. 3 indexed citations
8.
Zhu, Fei, et al.. (2023). SAPocket: Finding pockets on protein surfaces with a focus towards position and voxel channels. Expert Systems with Applications. 227. 120235–120235. 4 indexed citations
9.
Chen, Jie, Baihua Luo, Yanqin Li, et al.. (2023). Human umbilical cord mesenchymal stromal cell small extracellular vesicle transfer of microRNA-223-3p to lung epithelial cells attenuates inflammation in acute lung injury in mice. Journal of Nanobiotechnology. 21(1). 295–295. 15 indexed citations
10.
Broadhead, Matthew J., et al.. (2023). Synaptic expression of TAR-DNA-binding protein 43 in the mouse spinal cord determined using super-resolution microscopy. Frontiers in Molecular Neuroscience. 16. 1027898–1027898. 6 indexed citations
11.
Zhu, Fei, Fanwang Meng, Xin Ku, et al.. (2022). Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models. Journal of Chemical Information and Modeling. 62(14). 3331–3345. 13 indexed citations
12.
Fei-Fei, Li, et al.. (2020). Protein Interaction Network Reconstruction Through Ensemble Deep Learning With Attention Mechanism. Frontiers in Bioengineering and Biotechnology. 8. 390–390. 27 indexed citations
13.
Zhu, Fei, et al.. (2019). Cell‐type‐specific visualisation and biochemical isolation of endogenous synaptic proteins in mice. European Journal of Neuroscience. 51(3). 793–805. 15 indexed citations
14.
Frank, René, Fei Zhu, Noboru H. Komiyama, & Seth G. N. Grant. (2017). Hierarchical organization and genetically separable subfamilies of PSD 95 postsynaptic supercomplexes. Journal of Neurochemistry. 142(4). 504–511. 44 indexed citations
15.
Zhu, Fei, Quan Liu, Xiaofang Zhang, & Bairong Shen. (2015). Protein-Protein Interaction Network Constructing Based on Text Mining and Reinforcement Learning with Application to Prostate Cancer. 1306–1311. 1 indexed citations
16.
Zhu, Fei, et al.. (2014). A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination. The Scientific World JOURNAL. 2014. 1–7. 3 indexed citations
17.
Zhu, Fei, Quan Liu, Yuchen Fu, & Bairong Shen. (2014). Segmentation of Neuronal Structures Using SARSA (λ)-Based Boundary Amendment with Reinforced Gradient-Descent Curve Shape Fitting. PLoS ONE. 9(3). e90873–e90873. 3 indexed citations
18.
Miao, Wang, et al.. (2013). Quasi-Z-source inverter in grid-connected photovoltaic system. Chinese Control Conference. 7595–7599. 3 indexed citations
19.
Zhu, Fei. (2010). An integrated Optimized Traffic Monitoring System. Microcomputer Information. 1 indexed citations
20.
Zhu, Fei. (2006). Web-based Approach of Unified Identity Authorization. Jisuanji gongcheng. 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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