Fig. 4: Bayesian modelling training performance for S. cerevisiae ecGEM.
From: Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction
a, The r.m.s.e. for phenotype measurement and prediction during the Bayesian training process. b, Simulated exchange rates by posterior-mean-ecGEM (lines) compared with experimental data (dots). The kcat values in the posterior-mean-ecGEMs are mean values from 100 sampled posterior datasets obtained from the Bayesian training process. c, Principal component analysis for kcat datasets sampled during the Bayesian training approach, showing the progression from prior to posterior dataset. Each parameter in the set was standardized by subtracting the mean and then divided by the standard deviation before the principal component analysis. In blue are 100 sampled prior datasets; in red are 100 sampled posterior datasets; in grey are all other intermediate datasets. PC, principal component. d, The number of enzymes with a significantly changed mean value (Šidák-adjusted Welch’s t-test, P < 0.01, two-sided) and variance (Šidák-adjusted one-tailed F-test, P < 0.01) between the sampled prior and posterior kcat datasets. Parameters from 126 prior and 100 posterior ecGEMs were used for statistical tests. e, Variance distribution comparison for prior and posterior distribution. f, Correlation between deep learning-predicted kcat and posterior mean kcat. Student’s t-test was used to calculate P value for Pearson’s correlation.
