Massive computational acceleration by using neural networks to emulate mechanism-based biological models

Mechanistic models provide valuable insights, but large-scale simulations are computationally expensive. Here, the authors show that it is possible to explore the dynamics of a mechanistic model over a large set of parameters by training an artificial neural network on a smaller set of simulations.

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Detalles Bibliográficos
Autores principales: Shangying Wang, Kai Fan, Nan Luo, Yangxiaolu Cao, Feilun Wu, Carolyn Zhang, Katherine A. Heller, Lingchong You
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/ca202084bd924cf29c6afd7e9e5166b7
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Sumario:Mechanistic models provide valuable insights, but large-scale simulations are computationally expensive. Here, the authors show that it is possible to explore the dynamics of a mechanistic model over a large set of parameters by training an artificial neural network on a smaller set of simulations.