Jannis Born
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Computational Drug Discovery Methods
Papers in
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- Computational Drug Discovery Methods 13
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- COVID-19 diagnosis using AI 6
- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Matteo Manica (15 shared papers)María Rodríguez Martínez (6 shared papers)Ali Oskooei (3 shared papers)Anna Weber (1 shared paper)Julio Sáez-Rodríguez (1 shared paper)Vigneshwari Subramanian (1 shared paper)Nina Wiedemann (4 shared papers)Avinash Aujayeb (2 shared papers)
- Journals
- Patterns (2 papers)Journal of Chemical Information and Modeling (2 papers)Nature Machine Intelligence (2 papers)Molecular Pharmaceutics (1 paper)Computers and Education Artificial Intelligence (1 paper)
- Partner nations
- SwitzerlandUnited StatesGermany
In The Last Decade
Jannis Born
26 papers receiving 864 citations
Peers
Comparison fields: 5 of 105
- Health Informatics 68
- Computational Theory and Mathematics 292
- Critical Care and Intensive Care Medicine 66
- Radiology, Nuclear Medicine and Imaging 251
- Biophysics 29
Countries citing papers authored by Jannis Born
This map shows the geographic impact of Jannis Born'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 Jannis Born with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jannis Born more than expected).
Fields of papers citing papers by Jannis Born
This network shows the impact of papers produced by Jannis Born. 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 Jannis Born. The network helps show where Jannis Born may publish in the future.
Co-authors
The 25 scholars most cited alongside Jannis Born, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 114 | |
| 2 | 2021 | 112 | |
| 3 | 2019 | 102 | |
| 4 | 2023 | 93 | |
| 5 | 2021 | 68 | |
| 6 | 2020 | 62 | |
| 7 | 2020 | 51 | |
| 8 | 2021 | 44 | |
| 9 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models | 2020 | 36 |
| 10 | 2023 | 30 | |
| 11 | 2021 | 26 | |
| 12 | 2023 | 25 | |
| 13 | 2021 | 23 | |
| 14 | 2021 | 19 | |
| 15 | 2021 | 19 | |
| 16 | 2024 | 13 | |
| 17 | 2021 | 10 | |
| 18 | 2024 | 10 | |
| 19 | 2017 | 7 | |
| 20 | 2022 | 7 |
About Jannis Born
Jannis Born is a scholar working on Computational Theory and Mathematics, Radiology, Nuclear Medicine and Imaging, Critical Care and Intensive Care Medicine, Molecular Biology and Health Informatics, having authored 30 papers that have together received 890 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (13 papers), Machine Learning in Materials Science (8 papers), COVID-19 diagnosis using AI (6 papers), Protein Structure and Dynamics (5 papers), Microbial Natural Products and Biosynthesis (3 papers), Bioinformatics and Genomic Networks (3 papers), Ultrasound in Clinical Applications (3 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). The work is most often cited by research in Health Informatics (68 citations), Computational Theory and Mathematics (292 citations), Critical Care and Intensive Care Medicine (66 citations), Radiology, Nuclear Medicine and Imaging (251 citations) and Biophysics (29 citations). Jannis Born has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Matteo Manica, María Rodríguez Martínez, Ali Oskooei, Anna Weber, Julio Sáez-Rodríguez, Vigneshwari Subramanian, Nina Wiedemann, Avinash Aujayeb, Anwaar Ulhaq and Asim Ali Khan. Their work appears in journals such as Patterns, Journal of Chemical Information and Modeling, Nature Machine Intelligence, Molecular Pharmaceutics and Computers and Education Artificial Intelligence.
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.