Xiaohu Shi

2.4k total citations
64 papers, 1.7k citations indexed

About

Xiaohu Shi is a scholar working on Artificial Intelligence, Molecular Biology and Control and Systems Engineering. According to data from OpenAlex, Xiaohu Shi has authored 64 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 20 papers in Molecular Biology and 9 papers in Control and Systems Engineering. Recurrent topics in Xiaohu Shi's work include Machine Learning in Bioinformatics (11 papers), Metaheuristic Optimization Algorithms Research (9 papers) and Advanced Algorithms and Applications (8 papers). Xiaohu Shi is often cited by papers focused on Machine Learning in Bioinformatics (11 papers), Metaheuristic Optimization Algorithms Research (9 papers) and Advanced Algorithms and Applications (8 papers). Xiaohu Shi collaborates with scholars based in China, Singapore and United States. Xiaohu Shi's co-authors include Yanchun Liang, H. P. Lee, Chao Lu, Maurizio Marchese, Limin Wang, Renchu Guan, Jinhui Yang, Xueying Bai, Chunguo Wu and Chen Yang and has published in prestigious journals such as PLoS ONE, Journal of Power Sources and Scientific Reports.

In The Last Decade

Xiaohu Shi

55 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaohu Shi China 16 885 327 304 258 234 64 1.7k
Xiaodan Wang China 26 769 0.9× 333 1.0× 112 0.4× 272 1.1× 196 0.8× 127 2.7k
Thomas A. Runkler Germany 24 1.4k 1.6× 236 0.7× 217 0.7× 307 1.2× 178 0.8× 132 2.6k
Rafael Bello Cuba 25 1.3k 1.5× 401 1.2× 117 0.4× 177 0.7× 242 1.0× 148 2.5k
Ayed Salman Kuwait 16 1.2k 1.3× 412 1.3× 198 0.7× 190 0.7× 165 0.7× 48 2.1k
Zhifeng Hao China 24 913 1.0× 215 0.7× 125 0.4× 217 0.8× 129 0.6× 160 1.9k
Shih-Hsi Liu United States 14 1.3k 1.5× 717 2.2× 201 0.7× 170 0.7× 196 0.8× 40 2.0k
Eneko Osaba Spain 23 1.3k 1.5× 551 1.7× 528 1.7× 288 1.1× 231 1.0× 108 2.5k
Halife Kodaz Türkiye 15 714 0.8× 227 0.7× 286 0.9× 85 0.3× 112 0.5× 41 1.2k
Xingshi He China 19 1.1k 1.2× 399 1.2× 136 0.4× 309 1.2× 484 2.1× 39 2.1k
Feng Zou China 26 988 1.1× 569 1.7× 129 0.4× 195 0.8× 291 1.2× 127 2.2k

Countries citing papers authored by Xiaohu Shi

Since Specialization
Citations

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

Fields of papers citing papers by Xiaohu Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaohu Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaohu Shi. A scholar is included among the top collaborators of Xiaohu Shi 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 Xiaohu Shi. Xiaohu Shi 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.
Shi, Xiaohu, et al.. (2025). VTraceChain: A verifiable block chain with double-chain architecture for enhanced tracking. Journal of King Saud University - Computer and Information Sciences. 37(9).
2.
Wang, Zeyu, et al.. (2025). GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks. International Journal of Molecular Sciences. 26(3). 941–941.
3.
Chang, Ying, et al.. (2025). Dual prompt personalized federated learning in foundation models. Scientific Reports. 15(1). 28026–28026.
4.
Hong, Kibeom, Xiaohu Shi, Wei Pang, et al.. (2024). A deep learning based approach for automatic cardiac events identification. Biomedical Signal Processing and Control. 100. 107164–107164.
5.
Chen, Gang, Yubing Wen, Xiaohu Shi, et al.. (2024). Acquired Fanconi syndrome in mixed cryoglobulinemia patients: a single-center case series. Clinical Rheumatology. 43(7). 2329–2336. 1 indexed citations
6.
Cheng, Susan, Xiaohu Shi, Weijian Mao, & Tariq Alkhalifah. (2023). Seismic imaging enhancement of sparse ocean-bottom node data using deep learning. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 1–5. 1 indexed citations
7.
Qiao, Mu, Yanchun Liang, Adriano Tavares, & Xiaohu Shi. (2023). Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling. Entropy. 25(7). 973–973. 6 indexed citations
8.
9.
Zhang, Tianhao, et al.. (2022). Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network. Interdisciplinary Sciences Computational Life Sciences. 14(4). 937–946. 7 indexed citations
10.
Li, Ye & Xiaohu Shi. (2020). Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps. International Journal of Computational Intelligence and Applications. 19(3). 3 indexed citations
11.
Gao, Rui, et al.. (2020). Encoder–Decoder Couplet Generation Model Based on ‘Trapezoidal Context’ Character Vector. The Computer Journal. 64(3). 286–295. 1 indexed citations
12.
Tang, Chuan, Gui‐Ping Xu, Chunguo Wu, et al.. (2019). Surprisingly Popular Algorithm-Based Comprehensive Adaptive Topology Learning PSO. 2603–2610. 9 indexed citations
13.
Li, Ying, Xiaohu Shi, Yanchun Liang, et al.. (2017). RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation. BMC Bioinformatics. 18(1). 51–51. 9 indexed citations
14.
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
15.
Guan, Renchu, Chen Yang, Maurizio Marchese, Yanchun Liang, & Xiaohu Shi. (2014). Full Text Clustering and Relationship Network Analysis of Biomedical Publications. PLoS ONE. 9(9). e108847–e108847. 6 indexed citations
16.
Liang, Yanchun, et al.. (2014). A resampling ensemble algorithm for classification of imbalance problems. Neurocomputing. 143. 57–67. 78 indexed citations
17.
Wang, Han, Chao Zhang, Xiaohu Shi, Li Zhang, & You Zhou. (2012). Improving transmembrane protein consensus topology prediction using inter-helical interaction. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1818(11). 2679–2686. 6 indexed citations
18.
Shi, Xiaohu, et al.. (2011). A Sampling-Based Method for Ranking Protein Structural Models by Integrating Multiple Scores and Features. Current Protein and Peptide Science. 12(6). 540–548. 5 indexed citations
19.
Zhao, Dongyu, Yan Wang, Xiaohu Shi, et al.. (2010). PMirP: A pre-microRNA prediction method based on structure–sequence hybrid features. Artificial Intelligence in Medicine. 49(2). 127–132. 25 indexed citations
20.
Pei, Zhili, Xiaohu Shi, Maurizio Marchese, & Yanchun Liang. (2007). Text Categorization Method Based on Improved Mutual Information and Characteristic Weights Evaluation Algorithms. Institutional Research Information System (Università degli Studi di Trento). 34. 87–91. 2 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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