Dmitrii Bychkov
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Biophysics top 5%
- Cell Image Analysis Techniques
Papers in
-
- RNA and protein synthesis mechanisms 4
- Ubiquitin and proteasome pathways 1
-
- AI in cancer detection 6
- Co-authors
- Johan Lundin (8 shared papers)Riku Turkki (5 shared papers)Nina Linder (7 shared papers)Caj Haglund (4 shared papers)Mikael Lundin (3 shared papers)Stig Nordling (3 shared papers)Panu E. Kovanen (1 shared paper)Clare Verrill (1 shared paper)
- Journals
- Scientific Reports (4 papers)Cancer Research (3 papers)Bioinformatics (2 papers)Antimicrobial Agents and Chemotherapy (1 paper)International Archives of Allergy and Immunology (1 paper)
- Partner nations
- FinlandSwedenUnited Kingdom
In The Last Decade
Dmitrii Bychkov
19 papers receiving 882 citations
Peers
Comparison fields: 5 of 104
- Health Informatics 58
- Biophysics 94
- Radiology, Nuclear Medicine and Imaging 331
- Artificial Intelligence 383
- Oncology 201
Countries citing papers authored by Dmitrii Bychkov
This map shows the geographic impact of Dmitrii Bychkov'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 Dmitrii Bychkov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmitrii Bychkov more than expected).
Fields of papers citing papers by Dmitrii Bychkov
This network shows the impact of papers produced by Dmitrii Bychkov. 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 Dmitrii Bychkov. The network helps show where Dmitrii Bychkov may publish in the future.
Co-authors
The 25 scholars most cited alongside Dmitrii Bychkov, 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 | 2018 | 479 | |
| 2 | 2017 | 124 | |
| 3 | 2020 | 63 | |
| 4 | 2021 | 55 | |
| 5 | 2014 | 47 | |
| 6 | 2015 | 29 | |
| 7 | 2015 | 23 | |
| 8 | 2020 | 18 | |
| 9 | 2016 | 18 | |
| 10 | 2015 | 12 | |
| 11 | 2015 | 8 | |
| 12 | 2022 | 7 | |
| 13 | 2023 | 5 | |
| 14 | 2014 | 5 | |
| 15 | 2014 | 5 | |
| 16 | 2024 | 4 | |
| 17 | 2025 | 1 | |
| 18 | 2017 | 1 | |
| 19 | 2017 | 1 | |
| 20 | 2016 | 0 |
About Dmitrii Bychkov
Dmitrii Bychkov is a scholar working on Molecular Biology, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Epidemiology and Oncology, having authored 20 papers that have together received 905 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (6 papers), Influenza Virus Research Studies (5 papers), RNA and protein synthesis mechanisms (4 papers), Respiratory viral infections research (3 papers), Cell Image Analysis Techniques (2 papers), Prostate Cancer Treatment and Research (1 paper) and Ubiquitin and proteasome pathways (1 paper). The work is most often cited by research in Health Informatics (58 citations), Biophysics (94 citations), Radiology, Nuclear Medicine and Imaging (331 citations), Artificial Intelligence (383 citations) and Oncology (201 citations). Dmitrii Bychkov has collaborated with scholars based in Finland, Sweden and United Kingdom. Frequent co-authors include Johan Lundin, Riku Turkki, Nina Linder, Caj Haglund, Mikael Lundin, Stig Nordling, Panu E. Kovanen, Clare Verrill, Olli Kallioniemi and Katja Välimäki. Their work appears in journals such as Scientific Reports, Cancer Research, Bioinformatics, Antimicrobial Agents and Chemotherapy and International Archives of Allergy and Immunology.
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.