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

Abstract

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. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.

Here, the authors apply autoencoder neural networks to show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity, facilitating quantitative predictions and deduction of potential mechanisms.