Tomasz Danel
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- Computational Drug Discovery Methods 10
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- Machine Learning in Materials Science 7
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- Protein Structure and Dynamics 3
- Chemical Synthesis and Analysis 2
- Advanced Biosensing Techniques and Applications 1
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- Domain Adaptation and Few-Shot Learning 2
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- Radiomics and Machine Learning in Medical Imaging 1
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- Morphological variations and asymmetry 1
- Co-authors
- Łukasz MaziarkaMichał WarchołJan KaczmarczykKrzysztof RatajSabina PodlewskaIgor T. PodolakStanisław JastrzȩbskiJacek Tabor
- Journals
- Journal of Cheminformatics (2 papers)Journal of Chemical Information and Modeling (2 papers)Scientific Reports (1 paper)
- Partner nations
- PolandUnited StatesBelgium
In The Last Decade
Tomasz Danel
15 papers receiving 297 citations
Peers
Comparison fields: 5 of 76
- Computational Theory and Mathematics 191
- Health Informatics 8
- Materials Chemistry 139
- Biophysics 12
- Molecular Biology 142
Countries citing papers authored by Tomasz Danel
This map shows the geographic impact of Tomasz Danel'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 Tomasz Danel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomasz Danel more than expected).
Fields of papers citing papers by Tomasz Danel
This network shows the impact of papers produced by Tomasz Danel. 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 Tomasz Danel. The network helps show where Tomasz Danel may publish in the future.
Co-authorship network
The 24 scholars most cited alongside Tomasz Danel, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 14 | |
| 4 | 2024 | 7 | |
| 5 | 2024 | 2 | |
| 6 | 2023 | 7 | |
| 7 | 2023 | 27 | |
| 8 | 2023 | 21 | |
| 9 | 2023 | 11 | |
| 10 | 2022 | 29 | |
| 11 | 2022 | 2 | |
| 12 | 2022 | 3 | |
| 13 | 2021 | 8 | |
| 14 | 2021 | 1 | |
| 15 | Mol-CycleGAN: a generative model for molecular optimization | 2020 | 166 |
| 16 | Geometric Graph Convolutional Neural Networks. | 2019 | 5 |
About Tomasz Danel
Tomasz Danel is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Geometry and Topology, Pharmacology and Applied Mathematics, having authored 16 papers that have together received 304 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (10 papers), Machine Learning in Materials Science (7 papers), Protein Structure and Dynamics (3 papers), Chemical Synthesis and Analysis (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Morphological variations and asymmetry (1 paper) and Advanced Biosensing Techniques and Applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (191 citations), Health Informatics (8 citations), Materials Chemistry (139 citations), Biophysics (12 citations) and Molecular Biology (142 citations). Tomasz Danel has collaborated with scholars based in Poland, United States and Belgium. Frequent co-authors include Łukasz Maziarka, Michał Warchoł, Jan Kaczmarczyk, Krzysztof Rataj, Sabina Podlewska, Igor T. Podolak, Stanisław Jastrzȩbski, Jacek Tabor, Marcin Cieślak and Agnieszka Galanty. Their work appears in journals such as Journal of Cheminformatics, Journal of Chemical Information and Modeling, Scientific Reports, Biosystems Engineering and Computational and Structural Biotechnology Journal.
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.