DynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data.

ABSTRACT: The elucidation of gene regulatory networks is one of the major challenges of systems biology. Measurements about genes that are exploited by network inference methods are typically available either in the form of steady-state expression vectors or time series expression data. In our previous work, we proposed the GENIE3 method that exploits variable importance scores derived from Random forests to identify the regulators of each target gene. This method provided state-of-the-art performance on several benchmark datasets, but it could however not specifically be applied to time series expression data. We propose here an adaptation of the GENIE3 method, called dynamical GENIE3 (dynGENIE3), for handling both time series and steady-state expression data. The proposed method is evaluated exten

SUBMITTER: Huynh-Thu VA 

PROVIDER: S-EPMC5821733 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature