Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions
Fig 2
The predictive capability of the MiRNN outperforms an unconstrained RNN model using simulated data over a range of sparsity levels.
(a.) A comparison of the MiRNN architecture to a standard RNN, where the constraint highlighted in blue prevents the model from predicting the spontaneous emergence of a species. (b.) Schematic of simulated data generation, indicating that a ground truth computational bioreactor model is used to simulate species abundances over a time span of 130 hours, with measurements of species abundances taken at 26 hour intervals. (c.) Comparison of RNN (green) and MiRNN (orange) performance in species predictions according to the average Pearson correlation coefficient (R) over all species between predictions and measured values. The height of the bars and error bars correspond to the median and interquartile range in prediction performance after running 10-fold cross-validation over 5 trials, where samples were randomly shuffled in each trial. (d.) Same as in panel (c.), except that RMSE instead of Pearson correlation is shown.