Parametric and non-parametric gradient matching for network inference: a comparison.

ABSTRACT:

Background

Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. We limit our analysis to nonlinear differential equation based inference methods. In order to avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately.

Results

We apply a gradient matching inference approach to a large number of candidate models, including parametric differential equations or their corresponding non-parametric representations, we evaluate the network inference performance under various

SUBMITTER: Dony L 

PROVIDER: S-EPMC6346534 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature