Liang Ren
- Molecular Biology
- Infectious Diseases top 2%
- Oncology top 10%
- Neurology top 5%
- Cancer Research top 10%
- Co-authors
- Dong‐Hua YangJing‐Quan WangZhe‐Sheng ChenQingbin CuiPranav GuptaYehuda G. AssarafCharles R. AshbyLiuya Wei
- Topics
- COVID-19 Clinical Research Studies (8 papers)Beetle Biology and Toxicology Studies (5 papers)Forensic and Genetic Research (4 papers)
- Journals
- Nucleic Acids ResearchNature CommunicationsSHILAP Revista de lepidopterología
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Liang Ren
64 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Molecular Biology 678
- Infectious Diseases 595
- Oncology 252
- Neurology 247
- Cancer Research 224
Countries citing papers authored by Liang Ren
This map shows the geographic impact of Liang Ren'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 Liang Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang Ren more than expected).
Fields of papers citing papers by Liang Ren
This network shows the impact of papers produced by Liang Ren. 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 Liang Ren. The network helps show where Liang Ren may publish in the future.
Co-authorship network of co-authors of Liang Ren
This figure shows the co-authorship network connecting the top 25 collaborators of Liang Ren. A scholar is included among the top collaborators of Liang Ren 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 Liang Ren. Liang Ren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 17 | |
| 5 | 2 | |
| 6 | 12 | |
| 7 | Association Between Glucose Metabolism And Vascular Aging In Chinese Adults: A Cross-Sectional Analysis In The Tianning Cohort Study | 1 |
| 8 | 23 | |
| 9 | 7 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 6 | |
| 13 | 8 | |
| 14 | 9 | |
| 15 | 8 | |
| 16 | 6 | |
| 17 | 25 | |
| 18 | 10 | |
| 19 | 17 | |
| 20 | Quantitation of Mouse RGCs by Direct Labeling With Antibodies to Neurofilament, PGP 9.5, Osteopontin, and Brn3 Compared to Retrograde Labeling With Aminostilbamidine | 1 |
About Liang Ren
Liang Ren is a scholar working on Cancer Research, Transplantation and Toxicology, having authored 67 papers that have together received 2.0k indexed citations. Recurring topics across this work include COVID-19 Clinical Research Studies (8 papers), Beetle Biology and Toxicology Studies (5 papers) and Forensic and Genetic Research (4 papers). The work is most often cited by research in Infectious Diseases (595 citations), Neurology (247 citations) and Cancer Research (224 citations). Liang Ren has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Dong‐Hua Yang, Jing‐Quan Wang, Zhe‐Sheng Chen, Qingbin Cui, Pranav Gupta, Yehuda G. Assaraf, Charles R. Ashby, Liuya Wei, Liang Liu and Rongshuai Wang. Their work appears in journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.
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.