Jun Nie

541 citations
28 papers · 431 · h-index 10

Impact in

    • Cancer Immunotherapy and Biomarkers
    • Lung Cancer Research Studies
    • MicroRNA in disease regulation
    • Cancer-related molecular mechanisms research

Papers in

Jun Nie

27 papers receiving 425 citations

Peers

Jun Nie
Comparison fields: 5 of 43
  • Oncology 227
  • Cancer Research 63
  • Pulmonary and Respiratory Medicine 85
  • Immunology 44
  • Molecular Biology 89
Replace Xueyi Liang with:
Xueyi Liang China
Jikun Li China
Lihe Dai China
Teming Zhang China
Bing‐Hua Su Taiwan
Kristen Paige Bunch United States
Zheshen Jiang Belgium
Atsuko Umezawa Japan
Aurelia Pestka Germany
Rene-Filip Jackstadt United Kingdom
Jun Nie relative to Xueyi Liang China Xueyi Liang's profile →
Citations per field
00.5×1.5×2.1×
Xueyi Liang · 1×
Citations per year

Countries citing papers authored by Jun Nie

Since Specialization
Citations

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

Fields of papers citing papers by Jun Nie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015115
2 201947
3 202031
4 201630
5 202129
6 202126
7 201926
8 201820
9 201612
10 200411
11 201410
12 201910
13 20248
14 20167
15 20187
16 20227
17 20255
18 20235
19 20145
20 20214

About Jun Nie

Jun Nie is a scholar working on Oncology, Pulmonary and Respiratory Medicine, Internal Medicine, Hepatology and Cancer Research, having authored 28 papers that have together received 431 indexed citations. Recurring topics across this work include Lung Cancer Treatments and Mutations (8 papers), Cancer Immunotherapy and Biomarkers (6 papers), Lung Cancer Research Studies (5 papers), Lung Cancer Diagnosis and Treatment (4 papers), Venous Thromboembolism Diagnosis and Management (1 paper), Cancer Genomics and Diagnostics (1 paper), Hepatocellular Carcinoma Treatment and Prognosis (1 paper) and Nausea and vomiting management (1 paper). The work is most often cited by research in Oncology (227 citations), Cancer Research (63 citations), Pulmonary and Respiratory Medicine (85 citations), Immunology (44 citations) and Molecular Biology (89 citations). Jun Nie has collaborated with scholars based in China and Ethiopia. Frequent co-authors include Xiangjuan Ma, Weiheng Hu, Guangming Tian, Jindi Han, Ling Dai, Di Wu, Xiaoling Chen, Sen Han, Jie Zhang and Jian Hong Fang. Their work appears in journals such as Frontiers in Oncology, Future Oncology, Anti-Cancer Drugs, BMC Pulmonary Medicine and OncoTargets and Therapy.

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.

Explore authors with similar magnitude of impact