Xuekui Zhang

1.8k total citations
48 papers, 1.1k citations indexed

About

Xuekui Zhang is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Physiology. According to data from OpenAlex, Xuekui Zhang has authored 48 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 11 papers in Pulmonary and Respiratory Medicine and 6 papers in Physiology. Recurrent topics in Xuekui Zhang's work include Single-cell and spatial transcriptomics (7 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (7 papers) and Gene expression and cancer classification (6 papers). Xuekui Zhang is often cited by papers focused on Single-cell and spatial transcriptomics (7 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (7 papers) and Gene expression and cancer classification (6 papers). Xuekui Zhang collaborates with scholars based in Canada, United States and China. Xuekui Zhang's co-authors include Don D. Sin, Li Xing, Donald P. Tashkin, John E. Connett, Peter M.A. Calverley, Raphaël Gottardo, S. F. Paul Man, Finn Radner, Stephen I. Rennard and Ulf Sjöbring and has published in prestigious journals such as The Lancet, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Xuekui Zhang

44 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xuekui Zhang Canada 16 457 315 308 141 93 48 1.1k
Xiaoyin Li United States 20 144 0.3× 264 0.8× 232 0.8× 41 0.3× 156 1.7× 94 1.2k
Si Yang China 15 302 0.7× 58 0.2× 250 0.8× 112 0.8× 75 0.8× 18 1.2k
Jin Wook Kim South Korea 18 261 0.6× 54 0.2× 349 1.1× 24 0.2× 84 0.9× 116 1.2k
Yibing Ruan Canada 23 291 0.6× 145 0.5× 335 1.1× 41 0.3× 50 0.5× 90 1.7k
Dai Zhang China 19 257 0.6× 42 0.1× 297 1.0× 54 0.4× 74 0.8× 32 1.1k
Libby M. Morimoto United States 20 244 0.5× 157 0.5× 414 1.3× 70 0.5× 39 0.4× 68 1.7k
Shaoli Zhang China 17 171 0.4× 90 0.3× 328 1.1× 56 0.4× 193 2.1× 44 1.1k
Delphine Praud France 18 281 0.6× 81 0.3× 252 0.8× 283 2.0× 49 0.5× 57 1.6k
Gabriela Torres-Mejı́a Mexico 25 211 0.5× 112 0.4× 300 1.0× 35 0.2× 54 0.6× 82 1.7k

Countries citing papers authored by Xuekui Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Xuekui Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xuekui Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Xuekui Zhang. A scholar is included among the top collaborators of Xuekui Zhang 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 Xuekui Zhang. Xuekui Zhang 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.
Moa, Belaid, et al.. (2025). PCLDA: An interpretable cell annotation tool for single-cell RNA-sequencing data based on simple statistical methods. Computational and Structural Biotechnology Journal. 27. 3264–3274. 1 indexed citations
2.
Moa, Belaid, et al.. (2025). scSorterDL: a deep neural network-enhanced ensemble LDAs for single cell classifications. Briefings in Bioinformatics. 26(5).
3.
Naylor, Patti‐Jean, Karen Strange, Geoff D.C. Ball, et al.. (2025). Evaluating the Effectiveness of a Family-Based Lifestyle Intervention for Managing Childhood Overweight: Protocol for a Randomized Controlled Trial. JMIR Research Protocols. 14. e76837–e76837.
4.
Li, Xinrui, et al.. (2024). Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 9714–9726.
5.
Chen, Cong, Linda Sun, & Xuekui Zhang. (2023). A promising biomarker adaptive Phase 2/3 design – Explained and expanded. Contemporary Clinical Trials Communications. 36. 101229–101229. 1 indexed citations
6.
Tsao, Min, et al.. (2023). scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data. Bioinformatics Advances. 3(1). vbad030–vbad030. 12 indexed citations
7.
Li, Michael Lingzhi, et al.. (2023). Predicting the daily counts of COVID-19 infection using temporal convolutional networks. Journal of Global Health. 13. 3029–3029. 2 indexed citations
8.
Zhang, Xuekui, Yuying Huang, Ke Xu, & Li Xing. (2023). Novel modelling strategies for high-frequency stock trading data. Financial Innovation. 9(1). 39–39. 13 indexed citations
9.
Cao, Xiaowen, et al.. (2023). A Genome-Wide Association Study of Dementia Using the Electronic Medical Record. SHILAP Revista de lepidopterología. 3(1). 141–149. 2 indexed citations
10.
Shao, Xiaojian, Li Xing, Xuan Li, et al.. (2023). Single-Cell Sequencing of Lung Macrophages and Monocytes Reveals Novel Therapeutic Targets in COPD. Cells. 12(24). 2771–2771. 16 indexed citations
11.
Cheng, Xuanjin, Yongxing Liu, Jiahe Wang, et al.. (2022). cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines. Briefings in Bioinformatics. 23(3). 15 indexed citations
12.
Cao, Xiaowen, et al.. (2022). A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data. Frontiers in Genetics. 13. 836798–836798. 14 indexed citations
13.
Dong, Yao, Yao Dong, Shaoze Zhou, et al.. (2022). Deep learning methods may not outperform other machine learning methods on analyzing genomic studies. Frontiers in Genetics. 13. 992070–992070. 9 indexed citations
14.
Guo, Yang, et al.. (2021). Integrative COVID-19 biological network inference with probabilistic core decomposition. Briefings in Bioinformatics. 23(1). 6 indexed citations
15.
Huang, Xiaolin, et al.. (2021). The impact of lockdown timing on COVID-19 transmission across US counties. EClinicalMedicine. 38. 101035–101035. 31 indexed citations
16.
Xing, Li, et al.. (2021). Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data. SHILAP Revista de lepidopterología. 12(2). 317–334. 2 indexed citations
17.
Robertson, A. Gordon, Leping Li, Xuekui Zhang, et al.. (2012). Identification and analysis of murine pancreatic islet enhancers. Diabetologia. 56(3). 542–552. 45 indexed citations
18.
Sin, Don D., Bruce E. Miller, Annelyse Duvoix, et al.. (2011). Serum PARC/CCL-18 Concentrations and Health Outcomes in Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine. 183(9). 1187–1192. 84 indexed citations
19.
Man, S. F. Paul, Li Xing, John E. Connett, et al.. (2008). Circulating fibronectin to C-reactive protein ratio and mortality: a biomarker in COPD?. European Respiratory Journal. 32(6). 1451–1457. 48 indexed citations
20.
He, Jian‐Qing, Karey Shumansky, Xuekui Zhang, et al.. (2007). Polymorphisms of interleukin-10 and its receptor and lung function in COPD. European Respiratory Journal. 29(6). 1120–1126. 15 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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