On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
Inference of gene regulatory networks using s-system and differential evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Discovering Gene Networks with a Neural-Genetic Hybrid
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Reverse engineering of gene regulatory networks: a comparative study
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
Evolutionary computation in bioinformatics: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Reverse engineering of GRNs: an evolutionary approach based on the tsallis entropy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The discovery of the structure of genetic regulatory networks is of great interest for biologists and geneticists due to its pivotal role in organisms' metabolism. In the present paper we aim to investigate the inference power of genetic regulatory networks modeled as random boolean networks without the use of any prior biological information. The solutions space is explored by means of genetic algorithms, whose main goal is to find a consistent network given the target data obtained from biological experiments. We show that this approach succeeds in reconstructing a model with satisfactory level of accuracy, representing an useful tool to guide biologist towards the most probable interactions between the target genes.