Xing‐Yu Sun

1.6k total citations
48 papers, 1.3k citations indexed

About

Xing‐Yu Sun is a scholar working on Fluid Flow and Transfer Processes, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Xing‐Yu Sun has authored 48 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Fluid Flow and Transfer Processes, 13 papers in Molecular Biology and 12 papers in Materials Chemistry. Recurrent topics in Xing‐Yu Sun's work include Advanced Combustion Engine Technologies (19 papers), Machine Learning in Bioinformatics (13 papers) and Vehicle emissions and performance (10 papers). Xing‐Yu Sun is often cited by papers focused on Advanced Combustion Engine Technologies (19 papers), Machine Learning in Bioinformatics (13 papers) and Vehicle emissions and performance (10 papers). Xing‐Yu Sun collaborates with scholars based in China, United States and Netherlands. Xing‐Yu Sun's co-authors include Jian‐Ding Qiu, Ru‐Ping Liang, Shengbao Suo, Shaoping Shi, Shuyun Huang, Xiongbo Duan, Jinping Liu, Haifeng Liu, Jingping Liu and Banglin Deng and has published in prestigious journals such as PLoS ONE, Macromolecules and Analytical Biochemistry.

In The Last Decade

Xing‐Yu Sun

46 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xing‐Yu Sun China 23 583 415 234 221 199 48 1.3k
Lisheng Deng China 25 641 1.1× 197 0.5× 296 1.3× 218 1.0× 155 0.8× 80 1.8k
Yue Mu China 15 86 0.1× 154 0.4× 80 0.3× 114 0.5× 51 0.3× 58 1.1k
Françoise Couenne France 18 267 0.5× 53 0.1× 76 0.3× 111 0.5× 54 0.3× 72 932
Weiguo Chen China 18 45 0.1× 71 0.2× 324 1.4× 129 0.6× 104 0.5× 100 1.2k
Fouad Teymour United States 20 115 0.2× 58 0.1× 134 0.6× 328 1.5× 52 0.3× 72 1.3k
Jia Xu China 15 19 0.0× 405 1.0× 602 2.6× 437 2.0× 117 0.6× 90 1.3k
Gianluca Boccardo Italy 17 65 0.1× 32 0.1× 136 0.6× 156 0.7× 340 1.7× 46 909
Yasushi Tanaka Japan 16 80 0.1× 80 0.2× 245 1.0× 77 0.3× 44 0.2× 89 944
Hansong Xue Singapore 18 44 0.1× 31 0.1× 207 0.9× 189 0.9× 229 1.2× 44 1.3k

Countries citing papers authored by Xing‐Yu Sun

Since Specialization
Citations

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

Fields of papers citing papers by Xing‐Yu Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xing‐Yu Sun

This figure shows the co-authorship network connecting the top 25 collaborators of Xing‐Yu Sun. A scholar is included among the top collaborators of Xing‐Yu Sun 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 Xing‐Yu Sun. Xing‐Yu Sun 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, Wenjie, Zhixiong Huang, Bo Yin, et al.. (2025). Effects of Diesel Hydrocarbon Components on Combustion and Emissions in a Heavy-Duty Diesel Engine. Journal of Energy Engineering. 151(3).
2.
3.
Lu, Zhiyuan, et al.. (2024). Machine learning-based design of target property-oriented fuels using explainable artificial intelligence. Energy. 300. 131583–131583. 10 indexed citations
4.
Sun, Xing‐Yu, Mengjia Li, Jincheng Li, et al.. (2023). Nitrogen Oxides and Ammonia Removal Analysis Based on Three-Dimensional Ammonia-Diesel Dual Fuel Engine Coupled with One-Dimensional SCR Model. Energies. 16(2). 908–908. 26 indexed citations
5.
Wei, Yanju, et al.. (2022). Effects of Diesel Hydrocarbon Components on Cetane Number and Engine Combustion and Emission Characteristics. Applied Sciences. 12(7). 3549–3549. 17 indexed citations
6.
7.
Feng, Wei, et al.. (2022). Computational ghost imaging based on a conditional generation countermeasure network under a low sampling rate. Applied Optics. 61(32). 9693–9693. 3 indexed citations
8.
Duan, Xiongbo, Zhengxin Xu, Xing‐Yu Sun, Banglin Deng, & Jingping Liu. (2021). Effects of injection timing and EGR on combustion and emissions characteristics of the diesel engine fuelled with acetone–butanol–ethanol/diesel blend fuels. Energy. 231. 121069–121069. 98 indexed citations
9.
Jin, Chao, Xiyuan Zhang, Xichang Wang, et al.. (2019). Effects of polyoxymethylene dimethyl ethers on the solubility of ethanol/diesel and hydrous ethanol/diesel fuel blends. Energy Science & Engineering. 7(6). 2855–2865. 25 indexed citations
10.
Shi, Shaoping, Xing‐Yu Sun, Jian‐Ding Qiu, et al.. (2013). The prediction of palmitoylation site locations using a multiple feature extraction method. Journal of Molecular Graphics and Modelling. 40. 125–130. 26 indexed citations
11.
Sun, Xing‐Yu, Shaoping Shi, Jian‐Ding Qiu, et al.. (2012). Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform. Molecular BioSystems. 8(12). 3178–3184. 84 indexed citations
12.
Shi, Shaoping, Jian‐Ding Qiu, Xing‐Yu Sun, et al.. (2012). PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features. Molecular BioSystems. 8(5). 1520–1527. 67 indexed citations
13.
Shi, Shaoping, Jian‐Ding Qiu, Xing‐Yu Sun, et al.. (2012). PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme. PLoS ONE. 7(6). e38772–e38772. 73 indexed citations
14.
Suo, Shengbao, Jian‐Ding Qiu, Shaoping Shi, et al.. (2012). Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features. PLoS ONE. 7(11). e49108–e49108. 65 indexed citations
15.
Huang, Shuyun, Shaoping Shi, Jian‐Ding Qiu, et al.. (2012). PredSulSite: Prediction of protein tyrosine sulfation sites with multiple features and analysis. Analytical Biochemistry. 428(1). 16–23. 35 indexed citations
16.
Shi, Shaoping, Jian‐Ding Qiu, Xing‐Yu Sun, et al.. (2012). A method to distinguish between lysine acetylation and lysine methylation from protein sequences. Journal of Theoretical Biology. 310. 223–230. 23 indexed citations
17.
Liang, Ru‐Ping, Shuyun Huang, Shaoping Shi, et al.. (2011). A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization. Computers in Biology and Medicine. 42(2). 180–187. 19 indexed citations
18.
Qiu, Jian‐Ding, Xing‐Yu Sun, Shengbao Suo, et al.. (2011). Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: An approach from discrete wavelet transformation. Biochimie. 93(7). 1132–1138. 12 indexed citations
19.
Qiu, Jian‐Ding, Shengbao Suo, Xing‐Yu Sun, Shaoping Shi, & Ru‐Ping Liang. (2011). OligoPred: A web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou's pseudo amino acid composition. Journal of Molecular Graphics and Modelling. 30. 129–134. 49 indexed citations
20.
Sun, Xing‐Yu, et al.. (2006). Prediction of protein structural classes using support vector machines. Amino Acids. 30(4). 469–475. 111 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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