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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- CAR-T cell therapy research
- Cancer Immunotherapy and Biomarkers
Papers in
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- Bayesian Modeling and Causal Inference 4
- Gaussian Processes and Bayesian Inference 3
- Bayesian Methods and Mixture Models 2
- Co-authors
- Cristina Rius (1 shared paper)Jerome Samir (1 shared paper)Renske M. A. Vroomans (1 shared paper)Garry Dolton (1 shared paper)Dmitriy M. Chudakov (1 shared paper)Ivan V. Zvyagin (1 shared paper)Andrew Godkin (1 shared paper)Fabio Luciani (1 shared paper)
- Journals
- IEEE Robotics and Automation Letters (1 paper)Nucleic Acids Research (1 paper)Entropy (1 paper)Communications in computer and information science (2 papers)Software Impacts (1 paper)
- Partner nations
- NetherlandsRussiaGermany
In The Last Decade
Dmitry Bagaev
11 papers receiving 292 citations
Peers
Comparison fields: 5 of 62
- Immunology 146
- Oncology 59
- Radiology, Nuclear Medicine and Imaging 49
- Molecular Biology 127
- Transplantation 4
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 23 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 | 239 | |
| 2 | 2021 | 22 | |
| 3 | 2023 | 11 | |
| 4 | 2019 | 8 | |
| 5 | 2022 | 3 | |
| 6 | 2021 | 3 | |
| 7 | 2017 | 3 | |
| 8 | 2024 | 2 | |
| 9 | 2022 | 2 | |
| 10 | 2026 | 1 | |
| 11 | 2022 | 1 |
About Dmitry Bagaev
Dmitry Bagaev is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Computer Vision and Pattern Recognition and Ocean Engineering, having authored 11 papers that have together received 295 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (4 papers), Gaussian Processes and Bayesian Inference (3 papers), Geological Modeling and Analysis (2 papers), Reservoir Engineering and Simulation Methods (2 papers), Time Series Analysis and Forecasting (2 papers), Bayesian Methods and Mixture Models (2 papers), Advanced Data Processing Techniques (1 paper) and Image and Signal Denoising Methods (1 paper). The work is most often cited by research in Immunology (146 citations), Oncology (59 citations), Radiology, Nuclear Medicine and Imaging (49 citations), Molecular Biology (127 citations) and Transplantation (4 citations). Dmitry Bagaev has collaborated with scholars based in Netherlands, Russia and Germany. Frequent co-authors include Cristina Rius, Jerome Samir, Renske M. A. Vroomans, Garry Dolton, Dmitriy M. Chudakov, Ivan V. Zvyagin, Andrew Godkin, Fabio Luciani, Can Keşmir and Mikhail Shugay. Their work appears in journals such as IEEE Robotics and Automation Letters, Nucleic Acids Research, Entropy, Communications in computer and information science and Software Impacts.
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.