Evgeny Putin
Impact in
- Aging top 2%
- Genetics, Aging, and Longevity in Model Organisms
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- Computational Drug Discovery Methods
Papers in
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- Protein Structure and Dynamics 2
- Muscle Physiology and Disorders 1
- Bioinformatics and Genomic Networks 1
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- Computational Drug Discovery Methods 3
- Co-authors
- Alex Zhavoronkov (6 shared papers)Polina Mamoshina (4 shared papers)Armando Vieira (1 shared paper)Yan A. Ivanenkov (2 shared papers)Arip Asadulaev (2 shared papers)Vladimir Aladinskiy (1 shared paper)Alán Aspuru‐Guzik (1 shared paper)Benjamín Sánchez-Lengeling (1 shared paper)
- Journals
- Molecular Pharmaceutics (2 papers)Aging (2 papers)Frontiers in Genetics (1 paper)Journal of Chemical Information and Modeling (1 paper)The European Symposium on Artificial Neural Networks (1 paper)
- Partner nations
- RussiaUnited StatesUnited Kingdom
In The Last Decade
Evgeny Putin
7 papers receiving 1.3k citations
Evgeny Putin's Hit Papers
Peers
Comparison fields: 5 of 159
- Aging 107
- Computational Theory and Mathematics 488
- Health Informatics 34
- Biophysics 86
- Geriatrics and Gerontology 32
Countries citing papers authored by Evgeny Putin
This map shows the geographic impact of Evgeny Putin'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 Evgeny Putin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evgeny Putin more than expected).
Fields of papers citing papers by Evgeny Putin
This network shows the impact of papers produced by Evgeny Putin. 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 Evgeny Putin. The network helps show where Evgeny Putin may publish in the future.
Co-authors
The 25 scholars most cited alongside Evgeny Putin, 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 | Applications of Deep Learning in Biomedicine Hit paper breakdown → | 2016 | 472 |
| 2 | 2018 | 260 | |
| 3 | 2016 | 236 | |
| 4 | 2018 | 156 | |
| 5 | 2018 | 140 | |
| 6 | 2016 | 47 | |
| 7 | Pollen grain recognition using convolutional neural network. | 2018 | 23 |
About Evgeny Putin
Evgeny Putin is a scholar working on Molecular Biology, Computational Theory and Mathematics, Aging, Materials Chemistry and Pediatrics, Perinatology and Child Health, having authored 7 papers that have together received 1.3k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (3 papers), Genetics, Aging, and Longevity in Model Organisms (2 papers), Protein Structure and Dynamics (2 papers), Machine Learning in Materials Science (2 papers), Muscle Physiology and Disorders (1 paper), Plant and animal studies (1 paper), Bioinformatics and Genomic Networks (1 paper) and Spaceflight effects on biology (1 paper). The work is most often cited by research in Aging (107 citations), Computational Theory and Mathematics (488 citations), Health Informatics (34 citations), Biophysics (86 citations) and Geriatrics and Gerontology (32 citations). Evgeny Putin has collaborated with scholars based in Russia, United States and United Kingdom. Frequent co-authors include Alex Zhavoronkov, Polina Mamoshina, Armando Vieira, Yan A. Ivanenkov, Arip Asadulaev, Vladimir Aladinskiy, Alán Aspuru‐Guzik, Benjamín Sánchez-Lengeling, Alexander Aliper and Alexey Moskalev. Their work appears in journals such as Molecular Pharmaceutics, Aging, Frontiers in Genetics, Journal of Chemical Information and Modeling and The European Symposium on Artificial Neural Networks.
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.