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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- Protein Structure and Dynamics 5
- Bioinformatics and Genomic Networks 3
- Co-authors
- Matteo Manica (16 shared papers)María Rodríguez Martínez (7 shared papers)Anna Weber (1 shared paper)Ali Oskooei (3 shared papers)Vigneshwari Subramanian (1 shared paper)Julio Sáez-Rodríguez (1 shared paper)Avinash Aujayeb (2 shared papers)Nina Wiedemann (4 shared papers)
- Journals
- Patterns (2 papers)Journal of Chemical Information and Modeling (2 papers)Nature Communications (1 paper)iScience (1 paper)Applied Sciences (1 paper)
- Partner nations
- SwitzerlandGermanyUnited States
In The Last Decade
Jannis Born
23 papers receiving 760 citations
Peers
Comparison fields: 5 of 95
- Health Informatics 63
- Computational Theory and Mathematics 265
- Critical Care and Intensive Care Medicine 62
- Radiology, Nuclear Medicine and Imaging 234
- Biophysics 28
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 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 101 | |
| 2 | 2021 | 100 | |
| 3 | 2019 | 93 | |
| 4 | 2023 | 78 | |
| 5 | 2020 | 62 | |
| 6 | 2021 | 58 | |
| 7 | 2020 | 46 | |
| 8 | 2021 | 39 | |
| 9 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models | 2020 | 30 |
| 10 | 2021 | 25 | |
| 11 | 2023 | 23 | |
| 12 | 2021 | 23 | |
| 13 | 2023 | 23 | |
| 14 | 2021 | 17 | |
| 15 | 2021 | 17 | |
| 16 | 2024 | 11 | |
| 17 | 2021 | 11 | |
| 18 | 2024 | 5 | |
| 19 | 2022 | 5 | |
| 20 | 2017 | 4 |
About Jannis Born
Jannis Born is a scholar working on Computational Theory and Mathematics, Molecular Biology, Materials Chemistry, Radiology, Nuclear Medicine and Imaging and Pharmacology, having authored 26 papers that have together received 777 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), Radiomics and Machine Learning in Medical Imaging (3 papers), Bioinformatics and Genomic Networks (3 papers), Ultrasound in Clinical Applications (3 papers) and Microbial Natural Products and Biosynthesis (3 papers). The work is most often cited by research in Health Informatics (63 citations), Computational Theory and Mathematics (265 citations), Critical Care and Intensive Care Medicine (62 citations), Radiology, Nuclear Medicine and Imaging (234 citations) and Biophysics (28 citations). Jannis Born has collaborated with scholars based in Switzerland, Germany and United States. Frequent co-authors include Matteo Manica, María Rodríguez Martínez, Anna Weber, Ali Oskooei, Vigneshwari Subramanian, Julio Sáez-Rodríguez, Avinash Aujayeb, Nina Wiedemann, Anwaar Ulhaq and Asim Khan. Their work appears in journals such as Patterns, Journal of Chemical Information and Modeling, Nature Communications, iScience and Applied Sciences.
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.