Stefan Ganscha
Impact in
- Spectroscopy top 10%
- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
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- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- Gene Regulatory Network Analysis
Papers in
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- Protein Structure and Dynamics 3
- Gene Regulatory Network Analysis 3
- Single-cell and spatial transcriptomics 2
- Bioinformatics and Genomic Networks 2
- Pluripotent Stem Cells Research 1
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- Machine Learning in Materials Science 2
- Enzyme Structure and Function 1
- Co-authors
- Manfred Claassen (4 shared papers)Valentina Cappelletti (1 shared paper)Christian von Mering (1 shared paper)Paola Picotti (1 shared paper)Paul J. Boersema (1 shared paper)Abdullah Kahraman (1 shared paper)Sergii Kashubin (2 shared papers)Oliver T. Unke (2 shared papers)
- Journals
- Scientific Data (1 paper)Science Advances (1 paper)Cell Systems (1 paper)PLoS Computational Biology (1 paper)Science (1 paper)
- Partner nations
- SwitzerlandGermanySouth Korea
In The Last Decade
Stefan Ganscha
6 papers receiving 410 citations
Stefan Ganscha's Hit Papers
Peers
Comparison fields: 5 of 85
- Spectroscopy 83
- Molecular Biology 277
- Aging 7
- Computational Theory and Mathematics 45
- Materials Chemistry 115
Countries citing papers authored by Stefan Ganscha
This map shows the geographic impact of Stefan Ganscha'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 Stefan Ganscha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Ganscha more than expected).
Fields of papers citing papers by Stefan Ganscha
This network shows the impact of papers produced by Stefan Ganscha. 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 Stefan Ganscha. The network helps show where Stefan Ganscha may publish in the future.
Co-authors
The 24 scholars most cited alongside Stefan Ganscha, 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 | 2017 | 298 | |
| 2 | Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments Hit paper breakdown → | 2024 | 67 |
| 3 | 2016 | 21 | |
| 4 | 2016 | 14 | |
| 5 | 2025 | 11 | |
| 6 | 2019 | 4 |
About Stefan Ganscha
Stefan Ganscha is a scholar working on Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Structural Biology and Cell Biology, having authored 6 papers that have together received 415 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (3 papers), Gene Regulatory Network Analysis (3 papers), Single-cell and spatial transcriptomics (2 papers), Bioinformatics and Genomic Networks (2 papers), Computational Drug Discovery Methods (2 papers), Machine Learning in Materials Science (2 papers), Enzyme Structure and Function (1 paper) and Pluripotent Stem Cells Research (1 paper). The work is most often cited by research in Spectroscopy (83 citations), Molecular Biology (277 citations), Aging (7 citations), Computational Theory and Mathematics (45 citations) and Materials Chemistry (115 citations). Stefan Ganscha has collaborated with scholars based in Switzerland, Germany and South Korea. Frequent co-authors include Manfred Claassen, Valentina Cappelletti, Christian von Mering, Paola Picotti, Paul J. Boersema, Abdullah Kahraman, Sergii Kashubin, Oliver T. Unke, Michael Gastegger and Leonardo Medrano Sandonas. Their work appears in journals such as Scientific Data, Science Advances, Cell Systems, PLoS Computational Biology and Science.
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.