Vikas Ghai
Impact in
- Aging top 10%
- Genetics, Aging, and Longevity in Model Organisms
- Cancer Research top 10%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
Papers in ⓘ
- Aging 4
- Genetics, Aging, and Longevity in Model Organisms 4
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- MicroRNA in disease regulation 5
- Cancer-related molecular mechanisms research 3
- Co-authors
- Kai Wang (10 shared papers)Taek‐Kyun Kim (5 shared papers)Jeb Gaudet (3 shared papers)David J. Galas (2 shared papers)Takehito Shukuya (2 shared papers)Xiaogang Wu (2 shared papers)David P. Carbone (2 shared papers)Joseph M. Amann (2 shared papers)
- Journals
- Journal of Clinical Oncology (3 papers)Developmental Biology (2 papers)Blood (2 papers)Journal of Thoracic Oncology (1 paper)Journal of Clinical Medicine (1 paper)
- Partner nations
- United StatesCanadaDenmark
In The Last Decade
Vikas Ghai
22 papers receiving 378 citations
Peers
Comparison fields: 5 of 68
- Aging 34
- Cancer Research 191
- Molecular Biology 274
- Neurology 29
- Microbiology 11
Countries citing papers authored by Vikas Ghai
This map shows the geographic impact of Vikas Ghai'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 Vikas Ghai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vikas Ghai more than expected).
Fields of papers citing papers by Vikas Ghai
This network shows the impact of papers produced by Vikas Ghai. 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 Vikas Ghai. The network helps show where Vikas Ghai may publish in the future.
Co-authors
The 25 scholars most cited alongside Vikas Ghai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 78 | |
| 2 | 2020 | 59 | |
| 3 | 2017 | 51 | |
| 4 | 2019 | 36 | |
| 5 | 2020 | 35 | |
| 6 | 2019 | 20 | |
| 7 | 2018 | 18 | |
| 8 | 2008 | 17 | |
| 9 | 2021 | 17 | |
| 10 | 2011 | 11 | |
| 11 | 2012 | 11 | |
| 12 | 2007 | 8 | |
| 13 | 2012 | 5 | |
| 14 | 2018 | 4 | |
| 15 | 2017 | 3 | |
| 16 | 2006 | 1 | |
| 17 | 2018 | 1 | |
| 18 | 2009 | 1 | |
| 19 | 2011 | 1 | |
| 20 | 2006 | 1 |
About Vikas Ghai
Vikas Ghai is a scholar working on Aging, Cancer Research, Endocrine and Autonomic Systems, Genetics and Pathology and Forensic Medicine, having authored 23 papers that have together received 380 indexed citations. Recurring topics across this work include Extracellular vesicles in disease (6 papers), MicroRNA in disease regulation (5 papers), Genetics, Aging, and Longevity in Model Organisms (4 papers), Chronic Lymphocytic Leukemia Research (3 papers), Cancer-related molecular mechanisms research (3 papers), Lymphoma Diagnosis and Treatment (3 papers), Viral-associated cancers and disorders (2 papers) and CRISPR and Genetic Engineering (2 papers). The work is most often cited by research in Aging (34 citations), Cancer Research (191 citations), Molecular Biology (274 citations), Neurology (29 citations) and Microbiology (11 citations). Vikas Ghai has collaborated with scholars based in United States, Canada and Denmark. Frequent co-authors include Kai Wang, Taek‐Kyun Kim, Jeb Gaudet, David J. Galas, Takehito Shukuya, Xiaogang Wu, David P. Carbone, Joseph M. Amann, Tamio Okimoto and David Baxter. Their work appears in journals such as Journal of Clinical Oncology, Developmental Biology, Blood, Journal of Thoracic Oncology and Journal of Clinical Medicine.
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.