Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
AGN Simulation and Validation Model
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Identifying gene regulatory networks using evolutionary algorithms
Journal of Computing Sciences in Colleges
Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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The discovery of gene regulatory networks is a major goal in the field of bioinformatics due to their relevance, for instance, in the development of new drugs and medical treatments. The idea underneath this task is to recover gene interactions in a global and simple way, identifying the most significant connections and thereby generating a model to depict the mechanisms and dynamics of gene expression and regulation. In the present paper we tackle this challenge by applying a genetic algorithm to Boolean-based networks whose structures are inferred through the optimization of a Tsallis entropy function, which has been already successfully used in the inference of gene networks with other search schemes. Additionally, wisdom of crowds is applied to create a consensus network from the information contained within the last generation of the genetic algorithm. Results show that the proposed method is a promising approach and that the combination of criterion function based on Tsallis entropy with an heuristic search such as genetic algorithms yields networks up to 50% more accurate when compared to other Boolean-based approaches.