Lan Yang

2.9k citations
81 papers · 1.9k · 1 hit paper · h-index 23

Impact in

    • COVID-19 Clinical Research Studies
    • SARS-CoV-2 and COVID-19 Research
  • Neurology top 5%
    • Long-Term Effects of COVID-19

Papers in

Lan Yang

75 papers receiving 1.9k citations

Lan Yang's Hit Papers

The signal pathways and treatment of cytokine storm in COVID-19 2021 · 393 citations
3930+1+3Years since publication100200300

Peers

Lan Yang
Comparison fields: 5 of 150
  • Infectious Diseases 537
  • Neurology 254
  • Modeling and Simulation 68
  • Oncology 312
  • Immunology 190
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Citations per field
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Citations per year

Countries citing papers authored by Lan Yang

Since Specialization
Citations

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

Fields of papers citing papers by Lan Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.

#Work
1
The signal pathways and treatment of cytokine storm in COVID-19
Hit paper breakdown →
2021393
2 2006297
3 202077
4 202273
5 201772
6 202069
7 202160
8 202148
9 202043
10 202143
11 202042
12 201942
13 201235
14 201833
15 202131
16 202231
17 201630
18 202027
19 201227
20 202127

About Lan Yang

Lan Yang is a scholar working on Oncology, Pulmonary and Respiratory Medicine, Molecular Biology, Surgery and Radiology, Nuclear Medicine and Imaging, having authored 81 papers that have together received 1.9k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Lung Cancer Diagnosis and Treatment (9 papers), Lung Cancer Treatments and Mutations (7 papers), Cancer Immunotherapy and Biomarkers (6 papers), Lung Cancer Research Studies (6 papers), Remote Sensing and LiDAR Applications (5 papers), Remote Sensing in Agriculture (5 papers) and COVID-19 Clinical Research Studies (5 papers). The work is most often cited by research in Infectious Diseases (537 citations), Neurology (254 citations), Modeling and Simulation (68 citations), Oncology (312 citations) and Immunology (190 citations). Lan Yang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xueru Xie, Damo Xu, Jinrong Fu, Yufeng Zhou, Weimin Li, Bojiang Chen, Guangjin Tian, Yuncui Gan, Weimin Li and Jing Jin. Their work appears in journals such as Computers and Electronics in Agriculture, European Journal of Pharmacology, Biomedicine & Pharmacotherapy, Signal Transduction and Targeted Therapy and Annals of Translational Medicine.

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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