Janarthanan Krishnamoorthy
- Physiology top 5%
- Alzheimer's disease research and treatments 12
- Microbiology top 5%
- Antimicrobial Peptides and Activities 3
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- Computational Drug Discovery Methods 5
- Biomaterials top 10%
- Pharmacology top 10%
- Cholinesterase and Neurodegenerative Diseases 3
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- Protein Structure and Dynamics 8
- Lipid Membrane Structure and Behavior 4
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- Advanced Sensor and Energy Harvesting Materials 6
- Muscle activation and electromyography studies 5
- Co-authors
- Ayyalusamy RamamoorthyJeffrey BrenderAnirban BhuniaSubramanian VivekanandanAnirban GhoshMichele F. M. SciaccaE. Neil G. MarshYuta Suzuki
- Partner nations
- United StatesEthiopiaIndia
In The Last Decade
Janarthanan Krishnamoorthy
35 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 116
- Physiology 557
- Microbiology 132
- Computational Theory and Mathematics 188
- Biomaterials 135
- Pharmacology 150
Countries citing papers authored by Janarthanan Krishnamoorthy
This map shows the geographic impact of Janarthanan Krishnamoorthy'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 Janarthanan Krishnamoorthy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Janarthanan Krishnamoorthy more than expected).
Fields of papers citing papers by Janarthanan Krishnamoorthy
This network shows the impact of papers produced by Janarthanan Krishnamoorthy. 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 Janarthanan Krishnamoorthy. The network helps show where Janarthanan Krishnamoorthy may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Janarthanan Krishnamoorthy, 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 | 0 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 10 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 5 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 4 | |
| 9 | 2023 | 10 | |
| 10 | 2023 | 10 | |
| 11 | 2021 | 62 | |
| 12 | 2019 | 8 | |
| 13 | 2018 | 5 | |
| 14 | 2018 | 6 | |
| 15 | 2015 | 97 | |
| 16 | 2014 | 13 | |
| 17 | 2014 | 87 | |
| 18 | 2014 | 15 | |
| 19 | 2013 | 66 | |
| 20 | 2010 | 16 |
About Janarthanan Krishnamoorthy
Janarthanan Krishnamoorthy is a scholar working on Microbiology, Physiology and Computational Theory and Mathematics, having authored 36 papers that have together received 1.2k indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (12 papers), Protein Structure and Dynamics (8 papers), Advanced Sensor and Energy Harvesting Materials (6 papers), Computational Drug Discovery Methods (5 papers), Muscle activation and electromyography studies (5 papers), Lipid Membrane Structure and Behavior (4 papers), Antimicrobial Peptides and Activities (3 papers) and Cholinesterase and Neurodegenerative Diseases (3 papers). The work is most often cited by research in Physiology (557 citations), Microbiology (132 citations) and Computational Theory and Mathematics (188 citations). Janarthanan Krishnamoorthy has collaborated with scholars based in United States, Ethiopia and India. Frequent co-authors include Ayyalusamy Ramamoorthy, Jeffrey Brender, Anirban Bhunia, Subramanian Vivekanandan, Anirban Ghosh, Michele F. M. Sciacca, E. Neil G. Marsh, Yuta Suzuki, Carmelo La Rosa and Samuel A. Kotler.
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.