Lang Wang

3.0k citations
66 papers · 2.3k · 1 hit paper · h-index 22

Impact in

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

Papers in

Lang Wang

63 papers receiving 2.2k citations

Lang Wang's Hit Papers

Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up 2020 · 851 citations
8510+2+4Years since publication250500750

Peers

Lang Wang
Comparison fields: 5 of 138
  • Infectious Diseases 490
  • Neurology 303
  • Neurology 108
  • Applied Microbiology and Biotechnology 23
  • Geriatrics and Gerontology 35
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Lang Wang relative to Dawei Wang China Dawei Wang's profile →
Citations per field
00.5×2×3×4×5×
Dawei Wang · 1×
Citations per year

Countries citing papers authored by Lang Wang

Since Specialization
Citations

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

Fields of papers citing papers by Lang Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up
Hit paper breakdown →
2020851
2 2012112
3 2013102
4 201098
5 202270
6 201465
7 201062
8 201459
9 200952
10 202349
11 200847
12 201546
13 201045
14 201042
15 201338
16 201536
17 202035
18 201234
19 201033
20 201229

About Lang Wang

Lang Wang is a scholar working on Cancer Research, Molecular Biology, Geriatrics and Gerontology, Biophysics and Artificial Intelligence, having authored 66 papers that have together received 2.3k indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Genomics, phytochemicals, and oxidative stress (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Advanced battery technologies research (4 papers), Cervical Cancer and HPV Research (4 papers), COVID-19 Clinical Research Studies (3 papers), Reservoir Engineering and Simulation Methods (3 papers) and NF-κB Signaling Pathways (3 papers). The work is most often cited by research in Infectious Diseases (490 citations), Neurology (303 citations), Neurology (108 citations), Applied Microbiology and Biotechnology (23 citations) and Geriatrics and Gerontology (35 citations). Lang Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xiaomei Yu, Wenbo He, Dalong Hu, Hong Xiang Jiang, Jiali Zhou, Mingwei Bao, Huafen Liu, Hongliang Li, Lihua Zhu and Ling Yan. Their work appears in journals such as Molecular and Cellular Biochemistry, Hypertension, The Journal of Nutritional Biochemistry, Nature Communications and Journal of Petroleum Science and Engineering.

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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