Ming Lv

876 citations
46 papers · 706 · h-index 17

Impact in

Papers in

    • RNA Interference and Gene Delivery 4
    • Cell death mechanisms and regulation 4
    • Melanoma and MAPK Pathways 3
    • Advanced biosensing and bioanalysis techniques 3
    • HER2/EGFR in Cancer Research 3

Ming Lv

46 papers receiving 695 citations

Peers

Ming Lv
Comparison fields: 5 of 84
  • Cancer Research 111
  • Aging 12
  • Molecular Biology 331
  • Immunology 97
  • Complementary and alternative medicine 34
Replace Zheng Jiang with:
Zheng Jiang China
Shuangxi Li China
Dominic Jones United Kingdom
Delphine Gitenay France
Lin Zou China
Stanley Borowicz United States
Yuyu Yang China
Seon Rang Woo South Korea
Deborah Brancho United States
Ming Lv relative to Zheng Jiang China Zheng Jiang's profile →
Citations per field
00.5×1.5×2.3×
Zheng Jiang · 1×
Citations per year

Countries citing papers authored by Ming Lv

Since Specialization
Citations

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

Fields of papers citing papers by Ming Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201671
2 201259
3 201648
4 200941
5 201736
6 201526
7 201624
8 201924
9 202323
10 201422
11 201519
12 201819
13 201019
14 201518
15 201317
16 202216
17 201016
18 201815
19 202215
20 200514

About Ming Lv

Ming Lv is a scholar working on Molecular Biology, Oncology, Immunology, Cancer Research and Radiology, Nuclear Medicine and Imaging, having authored 46 papers that have together received 706 indexed citations. Recurring topics across this work include Monoclonal and Polyclonal Antibodies Research (5 papers), RNA Interference and Gene Delivery (4 papers), Cell death mechanisms and regulation (4 papers), HER2/EGFR in Cancer Research (3 papers), Melanoma and MAPK Pathways (3 papers), Advanced biosensing and bioanalysis techniques (3 papers), Immune Cell Function and Interaction (3 papers) and MicroRNA in disease regulation (3 papers). The work is most often cited by research in Cancer Research (111 citations), Aging (12 citations), Molecular Biology (331 citations), Immunology (97 citations) and Complementary and alternative medicine (34 citations). Ming Lv has collaborated with scholars based in China, Japan and Russia. Frequent co-authors include Jiannan Feng, Beifen Shen, Jing Wang, Jiyan Zhang, Xinying Li, Chunxia Qiao, Qingyang Wang, Xinjian Yang, Zhongxue Wu and Manman Zhao. Their work appears in journals such as Technology in Cancer Research & Treatment, Oncology Reports, Cellular and Molecular Immunology, Frontiers in Pharmacology and Clinical Radiology.

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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