Petrina Kamya
- Health Informatics top 10%
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- Computational Drug Discovery Methods 4
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- DNA and Nucleic Acid Chemistry 1
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- Machine Learning in Materials Science 2
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- Synthesis and Properties of Aromatic Compounds 2
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- Advanced Chemical Physics Studies 2
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- Meta-analysis and systematic reviews 1
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- Microbial Natural Products and Biosynthesis 1
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- Neural Networks and Reservoir Computing 1
- Co-authors
- Feng RenAlex ZhavoronkovAlex AliperDaniil PolykovskiyDmitry S. BezrukovHeidi M. MuchallYan A. IvanenkovBogdan Zagribelnyy
- Journals
- The Journal of Physical Chemistry A (2 papers)Clinical Pharmacology & Therapeutics (1 paper)Drug Discovery Today (1 paper)
- Partner nations
- CanadaHong KongUnited States
In The Last Decade
Petrina Kamya
8 papers receiving 288 citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Health Informatics 18
- Computational Theory and Mathematics 119
- Biophysics 18
- Aging 4
- Molecular Biology 138
Countries citing papers authored by Petrina Kamya
This map shows the geographic impact of Petrina Kamya'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 Petrina Kamya with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Petrina Kamya more than expected).
Fields of papers citing papers by Petrina Kamya
This network shows the impact of papers produced by Petrina Kamya. 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 Petrina Kamya. The network helps show where Petrina Kamya may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Petrina Kamya, 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 | PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discoverybreakdown → | 2024 | 65 |
| 2 | Chemistry42: An AI-Driven Platform for Molecular Design and Optimizationbreakdown → | 2023 | 109 |
| 3 | 2023 | 46 | |
| 4 | 2023 | 35 | |
| 5 | 2023 | 24 | |
| 6 | 2011 | 15 | |
| 7 | 2008 | 19 | |
| 8 | 2003 | 4 |
About Petrina Kamya
Petrina Kamya is a scholar working on Computational Theory and Mathematics, Statistics, Probability and Uncertainty and Statistics and Probability, having authored 8 papers that have together received 317 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (2 papers), Synthesis and Properties of Aromatic Compounds (2 papers), Advanced Chemical Physics Studies (2 papers), Meta-analysis and systematic reviews (1 paper), DNA and Nucleic Acid Chemistry (1 paper), Microbial Natural Products and Biosynthesis (1 paper) and Neural Networks and Reservoir Computing (1 paper). The work is most often cited by research in Health Informatics (18 citations), Computational Theory and Mathematics (119 citations) and Biophysics (18 citations). Petrina Kamya has collaborated with scholars based in Canada, Hong Kong and United States. Frequent co-authors include Feng Ren, Alex Zhavoronkov, Alex Aliper, Daniil Polykovskiy, Dmitry S. Bezrukov, Heidi M. Muchall, Yan A. Ivanenkov, Bogdan Zagribelnyy, Vladimir Aladinskiy and Yen‐Chu Lin. Their work appears in journals such as The Journal of Physical Chemistry A, Clinical Pharmacology & Therapeutics and Drug Discovery Today.
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.