Jun Mi

2.7k citations
54 papers · 2.2k · h-index 27

Impact in

    • Cancer, Hypoxia, and Metabolism
    • MicroRNA in disease regulation
    • Cancer-related molecular mechanisms research
  • Oncology top 5%
    • Cancer Cells and Metastasis

Papers in

    • DNA Repair Mechanisms 9
    • Metabolism, Diabetes, and Cancer 6
    • Epigenetics and DNA Methylation 5
    • Cancer, Hypoxia, and Metabolism 11
    • MicroRNA in disease regulation 5

Jun Mi

54 papers receiving 2.1k citations

Peers

Jun Mi
Comparison fields: 5 of 113
  • Cancer Research 809
  • Oncology 499
  • Molecular Biology 1.2k
  • Cell Biology 194
  • Acoustics and Ultrasonics 9
Replace Nathaniel D. Kirkpatrick with:
Nathaniel D. Kirkpatrick United States
Jiang Yang United States
Anika Nagelkerke Netherlands
Lu Liu China
Zhuo G. Chen United States
Alan Serrels United Kingdom
Kamiar Moin United States
Joseph Johnson United States
Jane Azizkhan‐Clifford United States
Tracy Vargo-Gogola United States
Jun Mi relative to Nathaniel D. Kirkpatrick United States Nathaniel D. Kirkpatrick's profile →
Citations per field
00.5×4.5×
Nathaniel D. Kirkpatrick · 1×
Citations per year

Countries citing papers authored by Jun Mi

Since Specialization
Citations

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

Fields of papers citing papers by Jun Mi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Mi, 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 Mi Line = papers co-authored together Jun Mi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015265
2 2005195
3 2013158
4 2017113
5 201992
6 201584
7 200977
8 200772
9 201471
10 201762
11 200561
12 201754
13 200652
14 200847
15 201445
16 201844
17 200444
18 201742
19 201439
20 201139

About Jun Mi

Jun Mi is a scholar working on Molecular Biology, Cancer Research, Oncology, Cell Biology and Immunology, having authored 54 papers that have together received 2.2k indexed citations. Recurring topics across this work include Cancer, Hypoxia, and Metabolism (11 papers), DNA Repair Mechanisms (9 papers), Metabolism, Diabetes, and Cancer (6 papers), MicroRNA in disease regulation (5 papers), Epigenetics and DNA Methylation (5 papers), Microtubule and mitosis dynamics (4 papers), Cancer-related Molecular Pathways (4 papers) and Quantum Dots Synthesis And Properties (4 papers). The work is most often cited by research in Cancer Research (809 citations), Oncology (499 citations), Molecular Biology (1.2k citations), Cell Biology (194 citations) and Acoustics and Ultrasonics (9 citations). Jun Mi has collaborated with scholars based in China, United States and Thailand. Frequent co-authors include Wei Ji, James M. Larner, Daoxiang Zhang, Jun He, Binhua P. Zhou, David L. Brautigan, Xiaodan Hou, Pan Sun, Shimin Zhao and Yongbin Wang. Their work appears in journals such as Theranostics, Cell Death and Disease, Cellular Signalling, Cell Death Discovery and FEBS Journal.

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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