Learning dynamical information from static protein and sequencing data
Reconstructing system dynamics on complex high-dimensional energy landscapes from static experimental snapshots remains challenging. Here, the authors introduce a framework to infer the essential dynamics of physical and biological systems without need for time-dependent measurements.
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| Main Authors: | , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2019
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| Subjects: | |
| Online Access: | https://doaj.org/article/ea15b726de5c44c49d48fefe125fa6ad |
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| Summary: | Reconstructing system dynamics on complex high-dimensional energy landscapes from static experimental snapshots remains challenging. Here, the authors introduce a framework to infer the essential dynamics of physical and biological systems without need for time-dependent measurements. |
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