Jyotika Varshney
- Molecular Biology
- Cancer Research
- Pulmonary and Respiratory Medicine
- Computational Theory and Mathematics top 10%
- Genetics
- Co-authors
- Subbaya SubramanianKaushik ChakravartyDavid A. LargaespadaMilcah C. ScottMaksim KhotimchenkoDaniel M. WaldenJaime F. ModianoAaron L. Sarver
- Topics
- Computational Drug Discovery Methods (8 papers)Virus-based gene therapy research (3 papers)Sarcoma Diagnosis and Treatment (3 papers)
- Partner nations
- United StatesNorwayPanama
In The Last Decade
Jyotika Varshney
24 papers receiving 464 citations
Peers
Comparison fields: 5 of 107
- Molecular Biology 207
- Cancer Research 93
- Pulmonary and Respiratory Medicine 89
- Computational Theory and Mathematics 67
- Genetics 56
Countries citing papers authored by Jyotika Varshney
This map shows the geographic impact of Jyotika Varshney'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 Jyotika Varshney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jyotika Varshney more than expected).
Fields of papers citing papers by Jyotika Varshney
This network shows the impact of papers produced by Jyotika Varshney. 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 Jyotika Varshney. The network helps show where Jyotika Varshney may publish in the future.
Co-authorship network of co-authors of Jyotika Varshney
This figure shows the co-authorship network connecting the top 25 collaborators of Jyotika Varshney. A scholar is included among the top collaborators of Jyotika Varshney 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 Jyotika Varshney. Jyotika Varshney 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 | 9 | |
| 3 | 6 | |
| 4 | 2 | |
| 5 | 19 | |
| 6 | 19 | |
| 7 | 49 | |
| 8 | 14 | |
| 9 | 29 | |
| 10 | 6 | |
| 11 | 84 | |
| 12 | 38 | |
| 13 | 28 | |
| 14 | 15 | |
| 15 | 35 | |
| 16 | 23 | |
| 17 | 3 | |
| 18 | 30 | |
| 19 | 25 | |
| 20 | Comprehensive computational analysis of different classes of Glutathione S-transferases in Triticum aestivum L. | 3 |
About Jyotika Varshney
Jyotika Varshney is a scholar working on Pharmaceutical Science, Computational Theory and Mathematics and Pharmacology, having authored 25 papers that have together received 493 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (8 papers), Virus-based gene therapy research (3 papers) and Sarcoma Diagnosis and Treatment (3 papers). The work is most often cited by research in Health Informatics (10 citations), Pharmaceutical Science (39 citations) and Cancer Research (93 citations). Jyotika Varshney has collaborated with scholars based in United States, Norway and Panama. Frequent co-authors include Subbaya Subramanian, Kaushik Chakravarty, David A. Largaespada, Milcah C. Scott, Maksim Khotimchenko, Daniel M. Walden, Jaime F. Modiano, Aaron L. Sarver, Susan K. Rathe and Logan G. Spector. Their work appears in journals such as Journal of Biological Chemistry, Cancer Research and Scientific Reports.
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.