Zongyang Lv

1.9k citations
23 papers · 1.3k indexed · 1 hit paper · h-index 15
Topics
Ubiquitin and proteasome pathways (7 papers)Glycosylation and Glycoproteins Research (4 papers)SARS-CoV-2 and COVID-19 Research (3 papers)
Partner nations
United StatesChinaPoland

In The Last Decade

Zongyang Lv

21 papers receiving 1.3k citations

Hit Papers

Activity profiling and crystal structures of inhibitor-bo...20202026202220242020100200300

Peers

Zongyang Lv
Comparison fields: 5 of 89
  • Molecular Biology 810
  • Infectious Diseases 390
  • Epidemiology 372
  • Immunology 247
  • Computational Theory and Mathematics 228
Replace Kai S. Yang with:
Kai S. Yang China
Shoudeng Chen China
Holly A. Saffran Canada
Yaoxing Wu China
Neerja Kaushik‐Basu United States
Jinzhi Tan China
Adeyemi O. Adedeji United States
Holger A. Lindner Germany
Yuri Kusov Germany
Anna M. Mielech United States
Zongyang Lv relative to Kai S. Yang China Kai S. Yang's profile →
Citations per field
00.5×1.5×1.9×
Kai S. Yang · 1×
Citations per year

Countries citing papers authored by Zongyang Lv

Since Specialization
Citations

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

Fields of papers citing papers by Zongyang Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zongyang Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Zongyang Lv. A scholar is included among the top collaborators of Zongyang Lv based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Zongyang Lv. Zongyang Lv is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 23
4 25
5 9
6 62
7 5
8 12
9 35
10 22
11 47
12 48
13 18
14 51
15 40
16 24
17 25
18 3
19 122
20 418

About Zongyang Lv

Zongyang Lv is a scholar working on Virology, Molecular Biology and Computational Theory and Mathematics, having authored 23 papers that have together received 1.3k indexed citations. Recurring topics across this work include Ubiquitin and proteasome pathways (7 papers), Glycosylation and Glycoproteins Research (4 papers) and SARS-CoV-2 and COVID-19 Research (3 papers). The work is most often cited by research in Infectious Diseases (390 citations), Computational Theory and Mathematics (228 citations) and Molecular Biology (810 citations). Zongyang Lv has collaborated with scholars based in United States, China and Poland. Frequent co-authors include Shaun K. Olsen, Marcin Drąg, Yingfang Liu, Tony T. Huang, Lingmin Yuan, Wioletta Rut, Stephanie Patchett, Miklós Békés, Digant Nayak and Mikołaj Żmudziński. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

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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