Jun Tan

693 citations
37 papers · 393 · h-index 11

Impact in

Papers in

    • RNA modifications and cancer 4
    • Kruppel-like factors research 3
    • Phytochemical compounds biological activities 3
    • Hedgehog Signaling Pathway Studies 3
    • Extracellular vesicles in disease 3
    • RNA Research and Splicing 2
    • Bacterial biofilms and quorum sensing 2
    • Advanced biosensing and bioanalysis techniques 2

Jun Tan

34 papers receiving 388 citations

Peers

Jun Tan
Comparison fields: 5 of 94
  • Molecular Medicine 15
  • Cancer Research 39
  • Molecular Biology 182
  • Reproductive Medicine 21
  • Biochemistry 14
Replace Xiaozhen Zhao with:
Xiaozhen Zhao China
Atefeh Araghi Iran
Nadine Dyballa‐Rukes Germany
Fangyuan Shao China
Woo Young Choi South Korea
Metwally M. Montaser Saudi Arabia
Hak Ryul Kim South Korea
Shaopeng Zhang China
Jun Tan relative to Xiaozhen Zhao China Xiaozhen Zhao's profile →
Citations per field
00.5×
Xiaozhen Zhao · 1×
Citations per year

Countries citing papers authored by Jun Tan

Since Specialization
Citations

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

Fields of papers citing papers by Jun Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Tan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Tan Line = papers co-authored together Jun Tan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202397
2 202340
3 201922
4 202415
5 201714
6 201713
7 201912
8 202312
9 202312
10 201911
11 202311
12 201910
13 202110
14 202110
15
[Synonymous codon usage bias in the rice cultivar 93-11 (Oryza sativa L. ssp. indica)].
20039
16 20219
17 20148
18 20188
19 20237
20 20217

About Jun Tan

Jun Tan is a scholar working on Molecular Biology, Cancer Research, Pulmonary and Respiratory Medicine, Ecology and Genetics, having authored 37 papers that have together received 393 indexed citations. Recurring topics across this work include RNA modifications and cancer (4 papers), Kruppel-like factors research (3 papers), Phytochemical compounds biological activities (3 papers), Hedgehog Signaling Pathway Studies (3 papers), Extracellular vesicles in disease (3 papers), RNA Research and Splicing (2 papers), Bacterial biofilms and quorum sensing (2 papers) and Advanced biosensing and bioanalysis techniques (2 papers). The work is most often cited by research in Molecular Medicine (15 citations), Cancer Research (39 citations), Molecular Biology (182 citations), Reproductive Medicine (21 citations) and Biochemistry (14 citations). Jun Tan has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Jidong Zhang, Zhixu He, Xiaofang Dai, Chunyang Li, Dan Zhang, Xianyao Wang, Ziqing Zhu, Shan Zeng, Hong Shen and Yuqi He. Their work appears in journals such as Medicine, Asian Journal of Andrology, Evidence-based Complementary and Alternative Medicine, Protein Expression and Purification and Current Microbiology.

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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