Chandrabose Selvaraj
- Molecular Biology top 10%
- Computational Theory and Mathematics top 0.5%
- Infectious Diseases top 5%
- Organic Chemistry top 10%
- Immunology
- Co-authors
- Sanjeev Kumar SinghSunil Kumar TripathiKarnati Konda ReddyPoonam C. SinghJung-Kul LeeVikash Kumar DubeyUmesh PanwarNeha Garg
- Topics
- Computational Drug Discovery Methods (26 papers)Biochemical and Structural Characterization (8 papers)SARS-CoV-2 and COVID-19 Research (8 papers)
- Cited by
- Computational Theory and MathematicsComplementary and alternative medicineInfectious Diseases
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEScientific Reports
- Partner nations
- IndiaSaudi ArabiaSouth Korea
In The Last Decade
Chandrabose Selvaraj
106 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 132
- Molecular Biology 1.1k
- Computational Theory and Mathematics 720
- Infectious Diseases 389
- Organic Chemistry 254
- Immunology 209
Countries citing papers authored by Chandrabose Selvaraj
This map shows the geographic impact of Chandrabose Selvaraj'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 Chandrabose Selvaraj with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chandrabose Selvaraj more than expected).
Fields of papers citing papers by Chandrabose Selvaraj
This network shows the impact of papers produced by Chandrabose Selvaraj. 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 Chandrabose Selvaraj. The network helps show where Chandrabose Selvaraj may publish in the future.
Co-authorship network of co-authors of Chandrabose Selvaraj
This figure shows the co-authorship network connecting the top 25 collaborators of Chandrabose Selvaraj. A scholar is included among the top collaborators of Chandrabose Selvaraj 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 Chandrabose Selvaraj. Chandrabose Selvaraj is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 4 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | 22 | |
| 7 | 8 | |
| 8 | 2 | |
| 9 | 13 | |
| 10 | 109 | |
| 11 | 194 | |
| 12 | 60 | |
| 13 | 30 | |
| 14 | 20 | |
| 15 | 14 | |
| 16 | 23 | |
| 17 | 31 | |
| 18 | 34 | |
| 19 | 33 | |
| 20 | Tool development for Prediction of pIC50 values from the IC50 values - A pIC50 value calculator | 43 |
About Chandrabose Selvaraj
Chandrabose Selvaraj is a scholar working on Computational Theory and Mathematics, Virology and Infectious Diseases, having authored 108 papers that have together received 2.3k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (26 papers), Biochemical and Structural Characterization (8 papers) and SARS-CoV-2 and COVID-19 Research (8 papers). The work is most often cited by research in Computational Theory and Mathematics (720 citations), Complementary and alternative medicine (191 citations) and Infectious Diseases (389 citations). Chandrabose Selvaraj has collaborated with scholars based in India, Saudi Arabia and South Korea. Frequent co-authors include Sanjeev Kumar Singh, Sunil Kumar Tripathi, Karnati Konda Reddy, Poonam C. Singh, Jung-Kul Lee, Vikash Kumar Dubey, Umesh Panwar, Neha Garg, Yamini Bhusan Tripathi and Radha Chaube. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE 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.