Jessica Vamathevan
- Molecular Biology top 10%
- Computational Theory and Mathematics top 0.5%
- Materials Chemistry top 10%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Co-authors
- Paul CzodrowskiShanrong ZhaoMichaela SpitzerEdgardo A. FerránParantu K. ShahIan DunhamAnant MadabhushiGeorge Lee
- Topics
- Sirtuins and Resveratrol in Medicine (3 papers)Genetics, Bioinformatics, and Biomedical Research (3 papers)Hepatitis B Virus Studies (3 papers)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Jessica Vamathevan
17 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 177
- Molecular Biology 1.1k
- Computational Theory and Mathematics 845
- Materials Chemistry 419
- Artificial Intelligence 214
- Biomedical Engineering 159
Countries citing papers authored by Jessica Vamathevan
This map shows the geographic impact of Jessica Vamathevan'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 Jessica Vamathevan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jessica Vamathevan more than expected).
Fields of papers citing papers by Jessica Vamathevan
This network shows the impact of papers produced by Jessica Vamathevan. 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 Jessica Vamathevan. The network helps show where Jessica Vamathevan may publish in the future.
Co-authorship network of co-authors of Jessica Vamathevan
This figure shows the co-authorship network connecting the top 25 collaborators of Jessica Vamathevan. A scholar is included among the top collaborators of Jessica Vamathevan 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 Jessica Vamathevan. Jessica Vamathevan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | Applications of machine learning in drug discovery and developmentbreakdown → | 1696 |
| 3 | 8 | |
| 4 | 14 | |
| 5 | 0 | |
| 6 | 6 | |
| 7 | 40 | |
| 8 | 10 | |
| 9 | 20 | |
| 10 | 26 | |
| 11 | 21 | |
| 12 | 1 | |
| 13 | 81 | |
| 14 | 31 | |
| 15 | 7 | |
| 16 | 75 | |
| 17 | 176 | |
| 18 | 44 |
About Jessica Vamathevan
Jessica Vamathevan is a scholar working on Geriatrics and Gerontology, Hepatology and Genetics, having authored 18 papers that have together received 2.3k indexed citations. Recurring topics across this work include Sirtuins and Resveratrol in Medicine (3 papers), Genetics, Bioinformatics, and Biomedical Research (3 papers) and Hepatitis B Virus Studies (3 papers). The work is most often cited by research in Health Informatics (94 citations), Computational Theory and Mathematics (845 citations) and Molecular Biology (1.1k citations). Jessica Vamathevan has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Paul Czodrowski, Shanrong Zhao, Michaela Spitzer, Edgardo A. Ferrán, Parantu K. Shah, Ian Dunham, Anant Madabhushi, George Lee, Dominic A. Clark and Bin Li. Their work appears in journals such as PLoS ONE, Nature Reviews Drug Discovery and Journal of Bacteriology.
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.