Bowei Yan

595 total citations
20 papers, 328 citations indexed

About

Bowei Yan is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Bowei Yan has authored 20 papers receiving a total of 328 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 3 papers in Artificial Intelligence. Recurrent topics in Bowei Yan's work include Computational Drug Discovery Methods (6 papers), Bioinformatics and Genomic Networks (5 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Bowei Yan is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Bioinformatics and Genomic Networks (5 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Bowei Yan collaborates with scholars based in China, United States and Sweden. Bowei Yan's co-authors include Song He, Lianlian Wu, Xiaochen Bo, Yuqi Wen, Chong Dai, Dongjin Leng, Yixin Zhang, Yuan Yao, Qingming Huang and Tingting Jiang and has published in prestigious journals such as Nucleic Acids Research, Expert Systems with Applications and Molecules.

In The Last Decade

Bowei Yan

18 papers receiving 322 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bowei Yan China 10 153 147 44 40 33 20 328
Evangelos Karatzas Greece 12 298 1.9× 75 0.5× 30 0.7× 28 0.7× 8 0.2× 29 475
David Belanger United States 14 426 2.8× 49 0.3× 59 1.3× 67 1.7× 28 0.8× 34 877
Lingfeng Zhang China 7 96 0.6× 109 0.7× 62 1.4× 27 0.7× 36 1.1× 30 266
Longqiang Luo China 7 285 1.9× 211 1.4× 55 1.3× 14 0.3× 21 0.6× 10 529
Jocelyne Bruand United States 6 383 2.5× 165 1.1× 39 0.9× 17 0.4× 51 1.5× 8 553
Haitao Fu China 6 202 1.3× 170 1.2× 36 0.8× 41 1.0× 58 1.8× 28 313
Xiaodong Zheng China 5 260 1.7× 186 1.3× 66 1.5× 27 0.7× 45 1.4× 11 342
Jake P. Taylor‐King United Kingdom 8 148 1.0× 80 0.5× 52 1.2× 17 0.4× 49 1.5× 14 283
Ka‐Lok Ng Taiwan 13 356 2.3× 139 0.9× 73 1.7× 93 2.3× 7 0.2× 59 510
Tomasz Danel Poland 8 142 0.9× 191 1.3× 32 0.7× 19 0.5× 139 4.2× 16 304

Countries citing papers authored by Bowei Yan

Since Specialization
Citations

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

Fields of papers citing papers by Bowei Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bowei Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Bowei Yan. A scholar is included among the top collaborators of Bowei Yan 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 Bowei Yan. Bowei Yan 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.
Wu, Lianlian, et al.. (2025). Multi-task multi-view and iterative error-correcting random forest for acute toxicity prediction. Expert Systems with Applications. 274. 126972–126972. 2 indexed citations
2.
Liao, Kai, Bowei Yan, Xiaodan Fan, et al.. (2025). X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development. Computers in Biology and Medicine. 188. 109787–109787. 1 indexed citations
3.
4.
Karwa, Vishesh, et al.. (2023). Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodels. Journal of the Royal Statistical Society Series B (Statistical Methodology). 86(1). 90–121. 3 indexed citations
5.
Cao, Liang, et al.. (2023). Response of exogenous melatonin on transcription and metabolism of soybean under drought stress. Physiologia Plantarum. 175(5). e14038–e14038. 12 indexed citations
6.
Yan, Bowei, Jing Wang, Lianlian Wu, et al.. (2022). An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning. Molecules. 27(10). 3112–3112. 9 indexed citations
7.
Wang, Jing, Qinglong Zhang, Caiyun Zhao, et al.. (2022). Computational methods, databases and tools for synthetic lethality prediction. Briefings in Bioinformatics. 23(3). 29 indexed citations
8.
Wu, Lianlian, Bowei Yan, Ruijiang Li, et al.. (2022). TOXRIC: a comprehensive database of toxicological data and benchmarks. Nucleic Acids Research. 51(D1). D1432–D1445. 53 indexed citations
9.
Zhong, Yuting, Bowei Yan, Kunhong Liu, et al.. (2022). A Multi-View Learning-Based Rule Extraction Algorithm For Accurate Hepatotoxicity Prediction. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 1423–1428. 3 indexed citations
10.
Wen, Yuqi, Xinyu Song, Bowei Yan, et al.. (2021). Multi-dimensional data integration algorithm based on random walk with restart. BMC Bioinformatics. 22(1). 97–97. 18 indexed citations
11.
He, Song, Xinyu Song, Yuqi Wen, et al.. (2021). COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data. PLoS Computational Biology. 17(3). e1008769–e1008769. 1 indexed citations
12.
Wu, Lianlian, et al.. (2021). Synthetic Lethal Interactions Prediction Based on Multiple Similarity Measures Fusion. Journal of Computer Science and Technology. 36(2). 261–275. 5 indexed citations
13.
Wu, Lianlian, Yixin Zhang, Yuqi Wen, et al.. (2021). An enhanced cascade-based deep forest model for drug combination prediction. Briefings in Bioinformatics. 23(2). 30 indexed citations
14.
Wu, Lianlian, Yuqi Wen, Dongjin Leng, et al.. (2021). Machine learning methods, databases and tools for drug combination prediction. Briefings in Bioinformatics. 23(1). 77 indexed citations
15.
Yan, Bowei, Purnamrita Sarkar, & Xiuyuan Cheng. (2017). Exact Recovery of Number of Blocks in Blockmodels. arXiv (Cornell University). 2 indexed citations
16.
Yan, Bowei, et al.. (2017). Statistical Convergence Analysis of Gradient EM on General Gaussian Mixture Models. arXiv (Cornell University). 6798–6808.
17.
Yan, Bowei, et al.. (2017). Convergence of Gradient EM on Multi-component Mixture of Gaussians. Neural Information Processing Systems. 30. 6956–6966. 12 indexed citations
18.
Yan, Bowei & Purnamrita Sarkar. (2016). On Robustness of Kernel Clustering. Neural Information Processing Systems. 29. 3090–3098. 5 indexed citations
19.
Crews, Kelley A., et al.. (2016). Analysis of the pattern of potential woody cover in Texas savanna. International Journal of Applied Earth Observation and Geoinformation. 52. 527–531. 15 indexed citations
20.
Xu, Qianqian, Qingming Huang, Tingting Jiang, et al.. (2012). HodgeRank on Random Graphs for Subjective Video Quality Assessment. IEEE Transactions on Multimedia. 14(3). 844–857. 51 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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