  An important problem in bioinformatics is the inference of gene regulatory
networks (GRN) from temporal expression profiles. In general, the main
limitations faced by GRN inference methods is the small number of samples with
huge dimensionalities and the noisy nature of the expression measurements. In
face of these limitations, alternatives are needed to get better accuracy on
the GRNs inference problem. This work addresses this problem by presenting an
alternative feature selection method that applies prior knowledge on its search
strategy, called SFFS-BA. The proposed search strategy is based on the
Sequential Floating Forward Selection (SFFS) algorithm, with the inclusion of a
scale-free (Barab\'asi-Albert) topology information in order to guide the
search process to improve inference. The proposed algorithm explores the
scale-free property by pruning the search space and using a power law as a
weight for reducing it. In this way, the search space traversed by the SFFS-BA
method combines a breadth-first search when the number of combinations is small
(<k> <= 2) with a depth-first search when the number of combinations becomes
explosive (<k> >= 3), being guided by the scale-free prior information.
Experimental results show that the SFFS-BA provides a better inference
similarities than SFS and SFFS, keeping the robustness of the SFS and SFFS
methods, thus presenting very good results.
