Jiuyong Li

9.3k total citations · 2 hit papers
238 papers, 5.8k citations indexed

About

Jiuyong Li is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Jiuyong Li has authored 238 papers receiving a total of 5.8k indexed citations (citations by other indexed papers that have themselves been cited), including 126 papers in Artificial Intelligence, 67 papers in Molecular Biology and 46 papers in Information Systems. Recurrent topics in Jiuyong Li's work include Bayesian Modeling and Causal Inference (33 papers), Privacy-Preserving Technologies in Data (32 papers) and Data Mining Algorithms and Applications (31 papers). Jiuyong Li is often cited by papers focused on Bayesian Modeling and Causal Inference (33 papers), Privacy-Preserving Technologies in Data (32 papers) and Data Mining Algorithms and Applications (31 papers). Jiuyong Li collaborates with scholars based in Australia, China and United States. Jiuyong Li's co-authors include Lin Liu, Thuc Duy Le, Jixue Liu, Murray J. Cairns, Raymond Chi-Wing Wong, Bingyu Sun, Ada Wai-Chee Fu, Andrea R. Gerson, Nobuyuki Kawashima and Junpeng Zhang and has published in prestigious journals such as Nature Communications, Bioinformatics and PLoS ONE.

In The Last Decade

Jiuyong Li

224 papers receiving 5.6k citations

Hit Papers

A review of the structure... 2006 2026 2012 2019 2013 2006 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
Jiuyong Li Australia 38 2.2k 1.6k 1.1k 717 557 238 5.8k
Xian Wu China 43 1.8k 0.8× 946 0.6× 247 0.2× 863 1.2× 396 0.7× 453 7.7k
Yanchun Liang China 37 2.1k 0.9× 1.4k 0.9× 454 0.4× 458 0.6× 229 0.4× 267 6.0k
Blaž Zupan Slovenia 41 1.3k 0.6× 4.3k 2.7× 688 0.6× 529 0.7× 247 0.4× 133 8.1k
Jonathan M. Garibaldi United Kingdom 39 2.4k 1.1× 1.0k 0.6× 363 0.3× 309 0.4× 348 0.6× 274 6.5k
Iñaki Inza Spain 29 2.9k 1.3× 2.8k 1.7× 355 0.3× 438 0.6× 328 0.6× 62 7.4k
Wenjia Li China 35 904 0.4× 950 0.6× 331 0.3× 624 0.9× 239 0.4× 197 4.3k
Yoichi Hayashi Japan 36 2.0k 0.9× 603 0.4× 362 0.3× 214 0.3× 147 0.3× 267 6.2k
Qingpeng Zhang China 37 956 0.4× 700 0.4× 63 0.1× 306 0.4× 214 0.4× 280 5.2k
Pablo Moscato Australia 28 2.1k 0.9× 1.3k 0.8× 512 0.5× 231 0.3× 158 0.3× 137 5.6k
Weixiong Zhang United States 50 1.6k 0.7× 3.2k 1.9× 1.0k 0.9× 297 0.4× 137 0.2× 213 8.4k

Countries citing papers authored by Jiuyong Li

Since Specialization
Citations

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

Fields of papers citing papers by Jiuyong Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiuyong Li

This figure shows the co-authorship network connecting the top 25 collaborators of Jiuyong Li. A scholar is included among the top collaborators of Jiuyong Li 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 Jiuyong Li. Jiuyong Li 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.
Yang, Jing, et al.. (2025). Meta-knowledge random attention update network for few-shot and anti-noise remaining useful life prediction. Advanced Engineering Informatics. 65. 103358–103358.
2.
Liu, Lin, et al.. (2025). Dependency-based anomaly detection: A general framework and comprehensive evaluation. Expert Systems with Applications. 297. 129249–129249.
4.
Cheng, Debo, et al.. (2024). Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11480–11488. 2 indexed citations
5.
Liu, Jixue, et al.. (2024). Cross-sensor transfer learning for fire smoke scene detection using variable-bands multi-spectral satellite imagery aided by spectral patterns. International Journal of Remote Sensing. 45(10). 3332–3348. 1 indexed citations
6.
Yu, Kui, et al.. (2024). FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 12235–12243. 3 indexed citations
7.
Zhang, Junpeng, et al.. (2024). Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data. BMC Biology. 22(1). 218–218. 3 indexed citations
8.
Shi, Zhining, Christopher W.K. Chow, Jing Gao, et al.. (2024). Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations. Sensors. 24(6). 1857–1857. 2 indexed citations
9.
Le, Thuc Duy, et al.. (2022). Decision Support for Disability Employment using Counterfactual Survival Analysis. 2022 IEEE International Conference on Big Data (Big Data). 30. 2103–2112.
10.
Cheng, Debo, Jiuyong Li, Lin Liu, et al.. (2022). Sufficient dimension reduction for average causal effect estimation. Data Mining and Knowledge Discovery. 36(3). 1174–1196. 6 indexed citations
11.
Pham, Vu, Lin Liu, Cameron P. Bracken, et al.. (2021). pDriver : a novel method for unravelling personalized coding and miRNA cancer drivers. Bioinformatics. 37(19). 3285–3292. 9 indexed citations
12.
Pham, Vu, et al.. (2020). A pseudotemporal causality approach to identifying miRNA–mRNA interactions during biological processes. Bioinformatics. 37(6). 807–814. 2 indexed citations
13.
Pham, Vu, Lin Liu, Cameron P. Bracken, et al.. (2020). DriverGroup : a novel method for identifying driver gene groups. Bioinformatics. 36(Supplement_2). i583–i591. 5 indexed citations
14.
Pham, Vu, Xiaomei Li, Buu Truong, et al.. (2020). The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge. Briefings in Bioinformatics. 22(3). 11 indexed citations
15.
Zhang, Junpeng, Taosheng Xu, Lin Liu, et al.. (2020). LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. PLoS Computational Biology. 16(4). e1007851–e1007851. 21 indexed citations
16.
Liu, Lin, et al.. (2019). A Graph is Worth a Thousand Words: Telling Event Stories using Timeline Summarization Graphs. 2565–2571. 15 indexed citations
17.
Sun, Youqiang, Jiuyong Li, Jixue Liu, Bingyu Sun, & Christopher W.K. Chow. (2014). An improvement of symbolic aggregate approximation distance measure for time series. Neurocomputing. 138. 189–198. 97 indexed citations
18.
Li, Jiuyong, et al.. (2009). Studying genotype-phenotype attack on k -anonymised medical and genomic data. 159–166. 2 indexed citations
19.
Sun, Xiaoxun, Hua Wang, & Jiuyong Li. (2008). Priority driven k -anonymisation for privacy protection. University of Southern Queensland ePrints (University of Southern Queensland). 73–78. 5 indexed citations
20.
He, Hongxing, et al.. (2006). Analysis of breast feeding data using data mining methods. University of Southern Queensland ePrints (University of Southern Queensland). 47–52. 4 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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