Ren Kong

1.8k citations
76 papers · 1.1k · h-index 17

Impact in

Papers in

Ren Kong

72 papers receiving 1.1k citations

Peers

Ren Kong
Comparison fields: 5 of 116
  • Computational Theory and Mathematics 238
  • Virology 57
  • Molecular Biology 580
  • Infectious Diseases 136
  • Health, Toxicology and Mutagenesis 85
Replace Vinay Kumar with:
Vinay Kumar India
Jeng‐Sen Tseng Taiwan
Vivek K. Vyas India
Sarah Naomi Bolz Germany
Sanjay Kumar Singh India
Chul Min Park South Korea
Olivier Heudi Switzerland
Wibke E. Diederich Germany
Vito Boido Italy
Keith C. Silverman United States
Ren Kong relative to Vinay Kumar India Vinay Kumar's profile →
Citations per field
00.5×1.5×2.5×
Vinay Kumar · 1×
Citations per year

Countries citing papers authored by Ren Kong

Since Specialization
Citations

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

Fields of papers citing papers by Ren Kong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020135
2 201475
3 201955
4 201853
5 202247
6 202046
7 201940
8 200634
9 202133
10 201531
11 201928
12 201926
13 202223
14 202223
15 202119
16 202218
17 201617
18 200816
19 202315
20 202115

About Ren Kong

Ren Kong is a scholar working on Molecular Biology, Computational Theory and Mathematics, Health, Toxicology and Mutagenesis, Oncology and Infectious Diseases, having authored 76 papers that have together received 1.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (16 papers), Protein Structure and Dynamics (10 papers), Environmental Toxicology and Ecotoxicology (9 papers), RNA and protein synthesis mechanisms (7 papers), HIV Research and Treatment (6 papers), Toxic Organic Pollutants Impact (6 papers), Mycotoxins in Agriculture and Food (5 papers) and HIV/AIDS drug development and treatment (5 papers). The work is most often cited by research in Computational Theory and Mathematics (238 citations), Virology (57 citations), Molecular Biology (580 citations), Infectious Diseases (136 citations) and Health, Toxicology and Mutagenesis (85 citations). Ren Kong has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Shan Chang, Stephen T.C. Wong, Chunsheng Liu, Jian Zhang, Guangbo Yang, Rui Xue, Xiaoqiang Guo, Ming Liu, Jianping Hu and Shaoyong Lu. Their work appears in journals such as Environmental Pollution, Chemosphere, Environmental Science & Technology, Aquatic Toxicology and Journal of Hazardous Materials.

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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