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Up to date, noise and other forms of mathematical error remains a persistent problem in GRNs inferred from high-throughput data. In particular, there is a systematic difference between a ground truth and the networks inferred from GRNs' simulated data. A set of comparison metrics to evaluate accuracy of inferred networks from experimental data have been developed [149, 168, 364, 365]. Furthermore, GNA and DIAGONA evaluate network structure and functions by some performance metrics such as, Area Under RECompENSATING (AU-ROC) or Area Under RECompENSATING & Failure (AU-ROC & FR), various classes of functional modules conservations [165]. However, these metrics are limited for probabilistic models such as Bayesian network and generative modeling approaches without the use of energy functions and the need to specify a prior for network parameters. Metrics that are more general are needed to compare models that attempt to learn a graphical representation of GRNs with noise. Challenges by ARACNE-TV estimates the significance of linear network models with add noise, which is similar to the one performed in GNA [149]. Similarly Dark et al. jointly compiled utilities and performance metrics from GRN inference with a reductionist experimental setup containing different noise models to measure the accuracy of GRNs [87]. However, it is unclear how to apply these metrics to modeling approaches such as Bayesian stochastic differential equation models, which aims at probabilistic models [217].
Network variability (as well as noise and non-linearity) is often not taken into account when inferring GRNs from high-throughput data. While a static Bayesian network may be sufficient to fit some data, it is usually unrealistic to assume that a GRN forms a constant network in time and space. d2c66b5586