Kannan Tharakaraman
- Infectious Diseases top 10%
- SARS-CoV-2 and COVID-19 Research 4
- Epidemiology top 10%
- Influenza Virus Research Studies 11
- Respiratory viral infections research 9
- Agronomy and Crop Science top 5%
- Modeling and Simulation top 10%
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- Genomics and Chromatin Dynamics 6
- RNA and protein synthesis mechanisms 4
- Glycosylation and Glycoproteins Research 3
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- Monoclonal and Polyclonal Antibodies Research 5
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- HIV Research and Treatment 3
- Co-authors
- Ram SasisekharanV. SasisekharanRahul RamanKarthik ViswanathanAkila JayaramanZachary ShriverJohn L. SpougeVidya Subramanian
- Journals
- Cell (2 papers)Proceedings of the National Academy of Sciences (3 papers)Nucleic Acids Research (1 paper)
- Partner nations
- United StatesSingaporeThailand
In The Last Decade
Kannan Tharakaraman
31 papers receiving 757 citations
Peers
Comparison fields: 5 of 84
- Infectious Diseases 228
- Epidemiology 377
- Agronomy and Crop Science 105
- Immunology 122
- Modeling and Simulation 23
Countries citing papers authored by Kannan Tharakaraman
This map shows the geographic impact of Kannan Tharakaraman'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 Kannan Tharakaraman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kannan Tharakaraman more than expected).
Fields of papers citing papers by Kannan Tharakaraman
This network shows the impact of papers produced by Kannan Tharakaraman. 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 Kannan Tharakaraman. The network helps show where Kannan Tharakaraman may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kannan Tharakaraman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 4 | |
| 2 | 2023 | 14 | |
| 3 | 2023 | 3 | |
| 4 | 2022 | 3 | |
| 5 | 2021 | 6 | |
| 6 | 2020 | 13 | |
| 7 | 2018 | 25 | |
| 8 | 2017 | 4 | |
| 9 | Glycan–protein interactions in viral pathogenesis | 2016 | 1 |
| 10 | 2016 | 98 | |
| 11 | 2015 | 40 | |
| 12 | Glycan receptor specificity as a useful tool for characterization and surveillance of influenza A virus | 2014 | 1 |
| 13 | 2014 | 55 | |
| 14 | 2014 | 24 | |
| 15 | 2013 | 107 | |
| 16 | 2013 | 20 | |
| 17 | 2009 | 5 | |
| 18 | 2008 | 29 | |
| 19 | 2006 | 2 | |
| 20 | 2005 | 29 |
About Kannan Tharakaraman
Kannan Tharakaraman is a scholar working on Virology, Infectious Diseases and Epidemiology, having authored 31 papers that have together received 783 indexed citations. Recurring topics across this work include Influenza Virus Research Studies (11 papers), Respiratory viral infections research (9 papers), Genomics and Chromatin Dynamics (6 papers), Monoclonal and Polyclonal Antibodies Research (5 papers), SARS-CoV-2 and COVID-19 Research (4 papers), RNA and protein synthesis mechanisms (4 papers), Glycosylation and Glycoproteins Research (3 papers) and HIV Research and Treatment (3 papers). The work is most often cited by research in Infectious Diseases (228 citations), Epidemiology (377 citations) and Agronomy and Crop Science (105 citations). Kannan Tharakaraman has collaborated with scholars based in United States, Singapore and Thailand. Frequent co-authors include Ram Sasisekharan, V. Sasisekharan, Rahul Raman, Karthik Viswanathan, Akila Jayaraman, Zachary Shriver, John L. Spouge, Vidya Subramanian, David Landsman and Gerald N. Wogan. Their work appears in journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.
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.