Virginia Gao
- Cellular and Molecular Neuroscience top 5%
- Neurology top 5%
- Neurology top 5%
- Molecular Biology
- Physiology top 10%
- Co-authors
- Cristina M. AlberiniMichael Q. SteinmanGiannina DescalziAkinobu SuzukiEmmanuel CruzBenjamin BessièresGabriella PolloniniDana Leifer
- Topics
- Neuroscience and Neuropharmacology Research (6 papers)Parkinson's Disease Mechanisms and Treatments (6 papers)Neuroinflammation and Neurodegeneration Mechanisms (4 papers)
- Cited by
- NeurologyBiological Psychiatry
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsJournal of Molecular Biology
- Partner nations
- United StatesUnited KingdomRussia
In The Last Decade
Virginia Gao
17 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 86
- Cellular and Molecular Neuroscience 378
- Neurology 370
- Neurology 260
- Molecular Biology 246
- Physiology 203
Countries citing papers authored by Virginia Gao
This map shows the geographic impact of Virginia Gao'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 Virginia Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Virginia Gao more than expected).
Fields of papers citing papers by Virginia Gao
This network shows the impact of papers produced by Virginia Gao. 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 Virginia Gao. The network helps show where Virginia Gao may publish in the future.
Co-authorship network of co-authors of Virginia Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Virginia Gao. A scholar is included among the top collaborators of Virginia Gao 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 Virginia Gao. Virginia Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 10 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 49 | |
| 7 | 35 | |
| 8 | 59 | |
| 9 | 65 | |
| 10 | 2 | |
| 11 | 233 | |
| 12 | 18 | |
| 13 | 130 | |
| 14 | 195 | |
| 15 | 92 | |
| 16 | 153 | |
| 17 | 14 | |
| 18 | 35 |
About Virginia Gao
Virginia Gao is a scholar working on Neurology, Neurology and Cellular and Molecular Neuroscience, having authored 18 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neuroscience and Neuropharmacology Research (6 papers), Parkinson's Disease Mechanisms and Treatments (6 papers) and Neuroinflammation and Neurodegeneration Mechanisms (4 papers). The work is most often cited by research in Neurology (260 citations), Neurology (370 citations) and Biological Psychiatry (53 citations). Virginia Gao has collaborated with scholars based in United States, United Kingdom and Russia. Frequent co-authors include Cristina M. Alberini, Michael Q. Steinman, Giannina Descalzi, Akinobu Suzuki, Emmanuel Cruz, Benjamin Bessières, Gabriella Pollonini, Dana Leifer, Pierre J. Magistretti and Sylvain Lengacher. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Molecular Biology.
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.