Andrew C. Li
- Molecular Biology top 1%
- Immunology top 1%
- Surgery top 2%
- Physiology top 2%
- Epidemiology top 2%
- Co-authors
- Christopher K. GlassTimothy M. WillsonMercedes RicoteCarolyn KellyWulf PalinskiKathleen K. BrownSumito OgawaValentina Perissi
- Topics
- Cholesterol and Lipid Metabolism (11 papers)Peroxisome Proliferator-Activated Receptors (11 papers)Cancer, Lipids, and Metabolism (5 papers)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Andrew C. Li
26 papers receiving 6.8k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Molecular Biology 4.6k
- Immunology 1.7k
- Surgery 1.4k
- Physiology 1.2k
- Epidemiology 1.2k
Countries citing papers authored by Andrew C. Li
This map shows the geographic impact of Andrew C. Li'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 Andrew C. Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew C. Li more than expected).
Fields of papers citing papers by Andrew C. Li
This network shows the impact of papers produced by Andrew C. Li. 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 Andrew C. Li. The network helps show where Andrew C. Li may publish in the future.
Co-authorship network of co-authors of Andrew C. Li
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew C. Li. A scholar is included among the top collaborators of Andrew C. Li 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 Andrew C. Li. Andrew C. Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds. | 10 |
| 3 | Bayesian Network Structure Learning with Side Constraints | 5 |
| 4 | 159 | |
| 5 | 52 | |
| 6 | 38 | |
| 7 | 91 | |
| 8 | 78 | |
| 9 | 84 | |
| 10 | 59 | |
| 11 | A SUMOylation-dependent pathway mediates transrepression of inflammatory response genes by PPAR-γbreakdown → | 1030 |
| 12 | 280 | |
| 13 | 497 | |
| 14 | 35 | |
| 15 | Peroxisome proliferator–activated receptor γ ligands inhibit development of atherosclerosis in LDL receptor–deficient micebreakdown → | 729 |
| 16 | The peroxisome proliferator-activated receptor-γ is a negative regulator of macrophage activationbreakdown → | 3082 |
| 17 | 53 | |
| 18 | 28 | |
| 19 | 3 | |
| 20 | 33 |
About Andrew C. Li
Andrew C. Li is a scholar working on Cancer Research, Immunology and Surgery, having authored 27 papers that have together received 6.8k indexed citations. Recurring topics across this work include Cholesterol and Lipid Metabolism (11 papers), Peroxisome Proliferator-Activated Receptors (11 papers) and Cancer, Lipids, and Metabolism (5 papers). The work is most often cited by research in Biochemistry (722 citations), Immunology (1.7k citations) and Cancer Research (1.1k citations). Andrew C. Li has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Christopher K. Glass, Timothy M. Willson, Mercedes Ricote, Carolyn Kelly, Wulf Palinski, Kathleen K. Brown, Sumito Ogawa, Valentina Perissi, Michael G. Rosenfeld and David W. Rose. 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.