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Feature selection is a crucial topic in pattern recognition applications, especially in the genetic regulatory networks (GRNs) inference problem which usually involves data with a large number of variables and small number of observations. In this context, the application of dimensionality reduction approaches such as those based on feature selection becomes a mandatory step in order to select the most important predictor genes that can explain some phenomena associated with the target genes. Given its importance in GRN inference, many feature selection methods (algorithms and criterion functions) have been proposed. However, it is decisive to validate such results in order to better understand its significance. The present work proposes a comparative study of feature selection techniques involving information theory concepts, applied to the estimation of GRNs from simulated temporal expression data generated by an artificial gene network (AGN) model. Four GRN inference methods are compared in terms of global network measures. Some interesting conclusions can be drawn from the experimental results.