Beibei Lv

486 citations
30 papers · 333 · h-index 11

Impact in

  • Toxicology top 10%
    • Bioactive Compounds and Antitumor Agents
    • Cancer-related molecular mechanisms research
    • MicroRNA in disease regulation

Papers in

Beibei Lv

27 papers receiving 326 citations

Peers

Beibei Lv
Comparison fields: 5 of 73
  • Toxicology 30
  • Cancer Research 78
  • Molecular Biology 199
  • Complementary and alternative medicine 15
  • Oncology 48
Replace Jui-Ling Hsu with:
Jui-Ling Hsu Taiwan
Chang Yan Chen United States
Youwei Zhang China
Heng-Chien Ho Taiwan
Chun Lei Yu China
Luquan Yang China
Il‐Kyung Chung South Korea
Yuxia Sui China
Melissa Gonzales United States
Beibei Lv relative to Jui-Ling Hsu Taiwan Jui-Ling Hsu's profile →
Citations per field
00.5×2.7×
Jui-Ling Hsu · 1×
Citations per year

Countries citing papers authored by Beibei Lv

Since Specialization
Citations

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

Fields of papers citing papers by Beibei Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202060
2 200835
3 201827
4 200924
5 200722
6 202022
7 202019
8 201916
9 201616
10 201614
11 202211
12 202310
13 202310
14 20228
15 20225
16 20095
17 20244
18 20224
19 20084
20 20094

About Beibei Lv

Beibei Lv is a scholar working on Molecular Biology, Plant Science, Oncology, Insect Science and Ecology, Evolution, Behavior and Systematics, having authored 30 papers that have together received 333 indexed citations. Recurring topics across this work include Insect-Plant Interactions and Control (4 papers), Lichen and fungal ecology (3 papers), CRISPR and Genetic Engineering (3 papers), Bryophyte Studies and Records (3 papers), Insect and Pesticide Research (3 papers), Plant biochemistry and biosynthesis (3 papers), Plant Molecular Biology Research (2 papers) and Plant Pathogens and Fungal Diseases (2 papers). The work is most often cited by research in Toxicology (30 citations), Cancer Research (78 citations), Molecular Biology (199 citations), Complementary and alternative medicine (15 citations) and Oncology (48 citations). Beibei Lv has collaborated with scholars based in China and Pakistan. Frequent co-authors include Jie Xing, Peng Gao, Ran‐Ran Ma, Lan Xiang, Guo‐Hao Zhang, Xiangyu Guo, Hui Zhang, Duan‐Bo Shi, Haiting Liu and Hong‐Xiang Lou. Their work appears in journals such as Rapid Communications in Mass Spectrometry, Frontiers in Plant Science, Foods, Plants and Chinese Journal of Natural Medicines.

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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