Jun Yue

1.4k total citations
50 papers, 1.1k citations indexed

About

Jun Yue is a scholar working on Infectious Diseases, Epidemiology and Molecular Biology. According to data from OpenAlex, Jun Yue has authored 50 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Infectious Diseases, 26 papers in Epidemiology and 16 papers in Molecular Biology. Recurrent topics in Jun Yue's work include Tuberculosis Research and Epidemiology (26 papers), Mycobacterium research and diagnosis (21 papers) and Cytokine Signaling Pathways and Interactions (6 papers). Jun Yue is often cited by papers focused on Tuberculosis Research and Epidemiology (26 papers), Mycobacterium research and diagnosis (21 papers) and Cytokine Signaling Pathways and Interactions (6 papers). Jun Yue collaborates with scholars based in China, United States and Hong Kong. Jun Yue's co-authors include Honghai Wang, Min Soo Han, Yanlin Zhao, Hongxiu Wang, Yao Li, Erliang Zeng, Jingping Xie, Wei Shi, Lirong Liu and Xuelian Zhang and has published in prestigious journals such as PLoS ONE, Oncogene and Journal of Clinical Microbiology.

In The Last Decade

Jun Yue

48 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
Jun Yue China 21 667 554 328 220 182 50 1.1k
Andrés Obregón‐Henao United States 21 763 1.1× 543 1.0× 299 0.9× 196 0.9× 285 1.6× 45 1.2k
Virginie Vasseur France 18 365 0.5× 476 0.9× 334 1.0× 105 0.5× 585 3.2× 32 1.3k
Zongde Zhang China 19 669 1.0× 486 0.9× 422 1.3× 254 1.2× 108 0.6× 76 1.1k
Domingo Palmero Argentina 21 855 1.3× 751 1.4× 166 0.5× 304 1.4× 317 1.7× 63 1.2k
Bingdong Zhu China 21 775 1.2× 434 0.8× 554 1.7× 132 0.6× 530 2.9× 65 1.3k
Daniel R. Roach Australia 11 875 1.3× 747 1.3× 299 0.9× 348 1.6× 695 3.8× 12 1.6k
Seónadh M. O’Leary Ireland 18 617 0.9× 489 0.9× 366 1.1× 117 0.5× 544 3.0× 21 1.3k
Geraldo M. B. Pereira Brazil 16 507 0.8× 398 0.7× 208 0.6× 254 1.2× 428 2.4× 31 1.1k
Qiyao Chai China 15 538 0.8× 436 0.8× 536 1.6× 105 0.5× 338 1.9× 23 1.2k
Gustavo Tapia Spain 19 622 0.9× 439 0.8× 303 0.9× 206 0.9× 332 1.8× 69 1.3k

Countries citing papers authored by Jun Yue

Since Specialization
Citations

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

Fields of papers citing papers by Jun Yue

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Yue

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Yue. A scholar is included among the top collaborators of Jun Yue 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 Jun Yue. Jun Yue 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.
2.
Chen, Long, Peng Zan, Zhanqiang Fang, et al.. (2025). An integrated DeepLabV3-transformer framework with dynamic weight adjustment for urban pollutant dispersion prediction. Process Safety and Environmental Protection. 201. 107509–107509.
3.
Li, Mengting, Jing Zhu, Jun Yue, et al.. (2024). Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities. Frontiers in Pharmacology. 15. 1366529–1366529. 1 indexed citations
4.
Luo, Liulin, Weiwei Chen, Li Liang, et al.. (2019). Polymorphism in the EREG gene confers susceptibility to tuberculosis. BMC Medical Genetics. 20(1). 7–7. 7 indexed citations
5.
Chen, Weiwei, et al.. (2019). Genetic Polymorphisms of miR-149 Associated with Susceptibility to Both Pulmonary and Extrapulmonary Tuberculosis. Genetic Testing and Molecular Biomarkers. 23(7). 442–447. 4 indexed citations
6.
Li, Liang, Huijuan Liu, Jun Yue, et al.. (2017). Association of Single-Nucleotide Polymorphism in the Hepcidin Promoter Gene with Susceptibility to Extrapulmonary Tuberculosis. Genetic Testing and Molecular Biomarkers. 21(6). 351–356. 9 indexed citations
7.
Wu, Hongjin, XY Zhang, Zhaoyang Hu, et al.. (2016). Evolution and heterogeneity of non-hereditary colorectal cancer revealed by single-cell exome sequencing. Oncogene. 36(20). 2857–2867. 54 indexed citations
8.
Li, Linlin, Jun Yue, Wenzhong Chen, et al.. (2015). The effects of SP110’s associated genes on fresh cavitary pulmonary tuberculosis in Han Chinese population. Clinical and Experimental Medicine. 16(2). 219–225. 10 indexed citations
9.
Pan, Bo, et al.. (2014). Dicer and its miRNAs are necessary gene and regulatory factors for differentiation and proliferation of vascular smooth muscle cell. PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS. 41(12). 1255–1264. 1 indexed citations
10.
11.
Ou, Xichao, Hui Xia, Qiang Li, et al.. (2014). A feasibility study of the Xpert MTB/RIF test at the peripheral level laboratory in China. International Journal of Infectious Diseases. 31. 41–46. 20 indexed citations
12.
Feng, Xiaoyan, Kun Chen, Xiqin Yang, et al.. (2012). Enhanced serodiagnostic utility of novel Mycobacterium tuberculosis polyproteins. Journal of Infection. 66(4). 366–375. 31 indexed citations
13.
Zhang, Lu, Qingzhong Wang, Wenjie Wang, et al.. (2012). Identification of putative biomarkers for the serodiagnosis of drug-resistant Mycobacterium tuberculosis. Proteome Science. 10(1). 12–12. 21 indexed citations
14.
Han, Min Soo, et al.. (2011). Relationship between single nucleotide polymorphism of interleukin-18 and susceptibility to pulmonary tuberculosis in the Chinese Han population. Microbiology and Immunology. 55(6). 388–393. 27 indexed citations
15.
Liang, Li, Jun Yue, Jianfang Liu, et al.. (2011). Interleukin-10 gene promoter polymorphisms and their protein production in pleural fluid in patients with tuberculosis. FEMS Immunology & Medical Microbiology. 62(1). 84–90. 23 indexed citations
16.
Yue, Jun, et al.. (2010). Analysis of the association between BTNL2 polymorphism and tuberculosis in Chinese Han population. Infection Genetics and Evolution. 10(4). 517–521. 15 indexed citations
17.
Zhang, Xuelian, Yanwei Hu, Shudan Chen, et al.. (2009). Synthesis and evaluation of (S,S)-N,N′-bis-[3-(2,2′,6,6′-tetramethylbenzhydryloxy)-2-hydroxy-propyl]-ethylenediamine (S2824) analogs with anti-tuberculosis activity. Bioorganic & Medicinal Chemistry Letters. 19(21). 6074–6077. 6 indexed citations
18.
Yue, Jun. (2004). Comparison of the proteome of Mycobacterium tuberculosis isoniazid susceptible strain with isoniazid resistant strain. Zhonghua weishengwuxue he mianyixue zazhi. 1 indexed citations
19.
Xie, Jianping, Yao Li, Jun Yue, et al.. (2003). ExploringMycobacterium tuberculosis infection-induced alterations in gene expression in macrophage by microarray hybridization. Science in China Series C Life Sciences. 46(4). 337–347. 3 indexed citations
20.
Xu, Yongzhong, Jianping Xie, Yao Li, et al.. (2003). Using a cDNA microarray to study cellular gene expression altered by Mycobacterium tuberculosis.. PubMed. 116(7). 1070–3. 28 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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