Floating search methods in feature selection
Pattern Recognition Letters
Coefficient of determination in nonlinear signal processing
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Signal Processing - Special issue: Genomic signal processing
Growing genetic regulatory networks from seed genes
Bioinformatics
A robust structural PGN model for control of cell-cycle progression stabilized by negative feedbacks
EURASIP Journal on Bioinformatics and Systems Biology
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
An improvement on floating search algorithms for feature subset selection
Pattern Recognition
IEEE Transactions on Signal Processing
Inferring Boolean network structure via correlation
Bioinformatics
Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Design of probabilistic Boolean networks under the requirement of contextual data consistency
IEEE Transactions on Signal Processing
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
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Models of gene regulatory networks (GRN) have been proposed along with algorithms for inferring their structure. By structure, we mean the relationships among the genes of the biological system under study. Despite the large number of genes found in the genome of an organism, it is believed that a small set of genes is responsible for maintaining a specific core regulatory mechanism (small subnetworks). We propose an algorithm for inference of subnetworks of genes from a small initial set of genes called seed and time series gene expression data. The algorithm has two main steps: First, it grows the seed of genes by adding genes to it, and second, it searches for subnetworks that can be biologically meaningful. The seed growing step is treated as a feature selection problem and we used a thresholded Boolean network with a perturbation model to design the criterion function that is used to select the features (genes). Given that the reverse engineering of GRN is a problem that does not necessarily have one unique solution, the proposed algorithm has as output a set of networks instead of one single network. The algorithm also analyzes the dynamics of the networks which can be time-consuming. Nevertheless, the algorithm is suitable when the number of genes is small. The results showed that the algorithm is capable of recovering an acceptable rate of gene interactions and to generate regulatory hypotheses that can be explored in the wet lab.