David Schnoerr
Impact in
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- Nonlinear Dynamics and Pattern Formation
Papers in
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- Gene Regulatory Network Analysis 9
- Bioinformatics and Genomic Networks 2
- Single-cell and spatial transcriptomics 2
- Diffusion and Search Dynamics 2
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- Cellular Automata and Applications 2
- Co-authors
- Michael P. H. Stumpf (4 shared papers)Mark Isalan (1 shared paper)Guido Sanguinetti (4 shared papers)Ramon Grima (3 shared papers)Sean T. Vittadello (1 shared paper)Rowan D. Brackston (1 shared paper)David F. Anderson (1 shared paper)Botond Cseke (1 shared paper)
- Journals
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (1 paper)Cell Systems (1 paper)PLoS Computational Biology (1 paper)The Journal of Chemical Physics (1 paper)Physical Review Letters (1 paper)
- Partner nations
- United KingdomAustraliaGermany
In The Last Decade
David Schnoerr
10 papers receiving 176 citations
Peers
Comparison fields: 5 of 50
- Biophysics 12
- Computer Networks and Communications 48
- Molecular Biology 111
- Statistical and Nonlinear Physics 20
- Modeling and Simulation 7
Countries citing papers authored by David Schnoerr
This map shows the geographic impact of David Schnoerr'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 David Schnoerr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Schnoerr more than expected).
Fields of papers citing papers by David Schnoerr
This network shows the impact of papers produced by David Schnoerr. 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 David Schnoerr. The network helps show where David Schnoerr may publish in the future.
Co-authors
The 11 scholars most cited alongside David Schnoerr, 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 | 2019 | 64 | |
| 2 | 2021 | 30 | |
| 3 | 2020 | 24 | |
| 4 | 2016 | 23 | |
| 5 | 2021 | 11 | |
| 6 | 2020 | 9 | |
| 7 | 2017 | 9 | |
| 8 | 2019 | 6 | |
| 9 | 2017 | 2 | |
| 10 | 2018 | 2 |
About David Schnoerr
David Schnoerr is a scholar working on Molecular Biology, Computational Theory and Mathematics, Computer Networks and Communications, Cellular and Molecular Neuroscience and Cognitive Neuroscience, having authored 10 papers that have together received 180 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (9 papers), Bioinformatics and Genomic Networks (2 papers), Single-cell and spatial transcriptomics (2 papers), Diffusion and Search Dynamics (2 papers), Cellular Automata and Applications (2 papers), Neuroscience and Neural Engineering (1 paper), Nonlinear Dynamics and Pattern Formation (1 paper) and Point processes and geometric inequalities (1 paper). The work is most often cited by research in Biophysics (12 citations), Computer Networks and Communications (48 citations), Molecular Biology (111 citations), Statistical and Nonlinear Physics (20 citations) and Modeling and Simulation (7 citations). David Schnoerr has collaborated with scholars based in United Kingdom, Australia and Germany. Frequent co-authors include Michael P. H. Stumpf, Mark Isalan, Guido Sanguinetti, Ramon Grima, Sean T. Vittadello, Rowan D. Brackston, David F. Anderson, Botond Cseke, Heike Siebert and Michael E. Rule. Their work appears in journals such as Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Cell Systems, PLoS Computational Biology, The Journal of Chemical Physics and Physical Review Letters.
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.