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Fig. 1 | Genome Biology

Fig. 1

From: MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Fig. 1

Schematic of the MDSINE software, which provides a comprehensive toolbox for dynamical systems analyses of microbiota time-series data. MDSINE implements a new algorithm, Bayesian Adaptive Penalized Counts Splines (BAPCS), for estimating microbial growth concentrations (trajectories) and their changes over time (gradients) from sequencing data; optionally, gradients can instead be estimated using our previously described first-order difference method. The software implements three new algorithms for dynamical systems inference: maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS). Our previously published method [8], the maximum likelihood unconstrained ridge regression algorithm (MLRR), is also implemented in MDSINE for comparison

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