Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 May 2024
27 May 2024
Historique:
received:
22
11
2023
accepted:
10
04
2024
medline:
28
5
2024
pubmed:
28
5
2024
entrez:
27
5
2024
Statut:
epublish
Résumé
Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.
Identifiants
pubmed: 38802422
doi: 10.1038/s41598-024-59378-9
pii: 10.1038/s41598-024-59378-9
doi:
Substances chimiques
Naloxone
36B82AMQ7N
Analgesics, Opioid
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12082Informations de copyright
© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
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