Fenglin Lv

2.0k citations
45 papers · 1.7k · h-index 21

Impact in

  • Neurology top 5%
    • Neuroinflammation and Neurodegeneration Mechanisms
    • Intracerebral and Subarachnoid Hemorrhage Research
  • Immunology top 10%
    • Immune Response and Inflammation
    • Immune cells in cancer

Papers in

Fenglin Lv

43 papers receiving 1.6k citations

Peers

Fenglin Lv
Comparison fields: 5 of 126
  • Neurology 281
  • Immunology 329
  • Neurology 225
  • Molecular Biology 866
  • Microbiology 64
Replace Kirti Sharma with:
Kirti Sharma India
Matthew Biancalana United States
Marc O. Anderson United States
John W. Trauger United States
Jifa Zhang China
R. Rajasekaran India
Laurent Désaubry France
Hervé Kovacic France
Stuart J. Conway United Kingdom
Zhiwei Feng United States
Fenglin Lv relative to Kirti Sharma India Kirti Sharma's profile →
Citations per field
00.5×4.6×
Kirti Sharma · 1×
Citations per year

Countries citing papers authored by Fenglin Lv

Since Specialization
Citations

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

Fields of papers citing papers by Fenglin Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012283
2 2008222
3 2006207
4 2014118
5 201585
6 200881
7 201766
8 200951
9 201037
10 201935
11 202034
12 200931
13
Role of Toll-like receptor 4/NF-kappaB pathway in monocyte-endothelial adhesion induced by low shear stress and ox-LDL.
200530
14 201528
15 201826
16 201825
17 200824
18 201723
19 201623
20 200722

About Fenglin Lv

Fenglin Lv is a scholar working on Molecular Biology, Computational Theory and Mathematics, Immunology, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 45 papers that have together received 1.7k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (9 papers), Computational Drug Discovery Methods (6 papers), Immune Response and Inflammation (4 papers), Monoclonal and Polyclonal Antibodies Research (4 papers), vaccines and immunoinformatics approaches (4 papers), Bacterial biofilms and quorum sensing (3 papers), Analytical Chemistry and Chromatography (3 papers) and RNA Research and Splicing (3 papers). The work is most often cited by research in Neurology (281 citations), Immunology (329 citations), Neurology (225 citations), Molecular Biology (866 citations) and Microbiology (64 citations). Fenglin Lv has collaborated with scholars based in China, Thailand and United States. Frequent co-authors include Peng Zhou, Qingwu Yang, Feifei Tian, Jing‐Zhou Wang, Zhicai Shang, Yu Zhou, Sen Lin, Jie Cui, Yang Li and Qi Zhong. Their work appears in journals such as Frontiers in Microbiology, Oncotarget, Journal of Theoretical Biology, Amino Acids and Biochemical and Biophysical Research Communications.

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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