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Biologically-informed neural networks guide mechanistic modeling from sparse experimental data
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approxima...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732115/ https://www.ncbi.nlm.nih.gov/pubmed/33259472 http://dx.doi.org/10.1371/journal.pcbi.1008462 |