Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics

The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. Th...

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Detalles Bibliográficos
Autores principales: Baig, Yasa, Ma, Helena R., Xu, Helen, You, Lingchong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696002/
https://www.ncbi.nlm.nih.gov/pubmed/38049401
http://dx.doi.org/10.1038/s41467-023-43455-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696002/
https://www.ncbi.nlm.nih.gov/pubmed/38049401
http://dx.doi.org/10.1038/s41467-023-43455-0

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