Dmitry Bagaev
Impact in
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- T-cell and B-cell Immunology
- Immunotherapy and Immune Responses
- Immune Cell Function and Interaction
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- Monoclonal and Polyclonal Antibodies Research
Papers in
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- Bayesian Modeling and Causal Inference 4
- Gaussian Processes and Bayesian Inference 3
- Bayesian Methods and Mixture Models 2
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- Time Series Analysis and Forecasting 2
- Co-authors
- Alexander Greenshields‐Watson (1 shared paper)Meriem Attaf (1 shared paper)Fabio Luciani (1 shared paper)Evgeny S. Egorov (1 shared paper)Can Keşmir (1 shared paper)Nina Babel (1 shared paper)Ivan V. Zvyagin (1 shared paper)Jerome Samir (1 shared paper)
- Journals
- IEEE Robotics and Automation Letters (1 paper)Nucleic Acids Research (1 paper)Entropy (1 paper)Software Impacts (1 paper)2022 30th European Signal Processing Conference (EUSIPCO) (1 paper)
- Partner nations
- NetherlandsAustraliaCanada
In The Last Decade
Dmitry Bagaev
7 papers receiving 259 citations
Peers
Comparison fields: 5 of 50
- Immunology 162
- Radiology, Nuclear Medicine and Imaging 52
- Oncology 60
- Transplantation 5
- Molecular Biology 127
Countries citing papers authored by Dmitry Bagaev
This map shows the geographic impact of Dmitry Bagaev'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 Dmitry Bagaev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmitry Bagaev more than expected).
Fields of papers citing papers by Dmitry Bagaev
This network shows the impact of papers produced by Dmitry Bagaev. 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 Dmitry Bagaev. The network helps show where Dmitry Bagaev may publish in the future.
Co-authors
The 20 scholars most cited alongside Dmitry Bagaev, 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 | 2019 | 229 | |
| 2 | 2021 | 16 | |
| 3 | 2023 | 9 | |
| 4 | 2021 | 3 | |
| 5 | 2022 | 2 | |
| 6 | 2022 | 1 | |
| 7 | 2022 | 1 | |
| 8 | 2026 | 0 | |
| 9 | 2024 | 0 |
About Dmitry Bagaev
Dmitry Bagaev is a scholar working on Artificial Intelligence, Signal Processing, Computer Networks and Communications, Computer Vision and Pattern Recognition and Molecular Biology, having authored 9 papers that have together received 261 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (4 papers), Gaussian Processes and Bayesian Inference (3 papers), Time Series Analysis and Forecasting (2 papers), Bayesian Methods and Mixture Models (2 papers), Optimization and Search Problems (1 paper), Error Correcting Code Techniques (1 paper), Indoor and Outdoor Localization Technologies (1 paper) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Immunology (162 citations), Radiology, Nuclear Medicine and Imaging (52 citations), Oncology (60 citations), Transplantation (5 citations) and Molecular Biology (127 citations). Dmitry Bagaev has collaborated with scholars based in Netherlands, Australia and Canada. Frequent co-authors include Alexander Greenshields‐Watson, Meriem Attaf, Fabio Luciani, Evgeny S. Egorov, Can Keşmir, Nina Babel, Ivan V. Zvyagin, Jerome Samir, Garry Dolton and Dmitriy M. Chudakov. Their work appears in journals such as IEEE Robotics and Automation Letters, Nucleic Acids Research, Entropy, Software Impacts and 2022 30th European Signal Processing Conference (EUSIPCO).
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.