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Open AccessArticle

Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks

1
Computer Science Division, Pablo de Olavide University, ES-41013 Seville, Spain
2
Faculty of Experimental Sciences, Pablo de Olavide University, ES-41013 Seville, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1139; https://doi.org/10.3390/e21121139
Received: 1 November 2019 / Accepted: 13 November 2019 / Published: 21 November 2019
(This article belongs to the Special Issue Application of Information Theory in Biomedical Data Mining)
Gene networks have become a powerful tool in the comprehensive analysis of gene expression. Due to the increasing amount of available data, computational methods for networks generation must deal with the so-called curse of dimensionality in the quest for the reliability of the obtained results. In this context, ensemble strategies have significantly improved the precision of results by combining different measures or methods. On the other hand, structure optimization techniques are also important in the reduction of the size of the networks, not only improving their topology but also keeping a positive prediction ratio. In this work, we present Ensemble and Greedy networks (EnGNet), a novel two-step method for gene networks inference. First, EnGNet uses an ensemble strategy for co-expression networks generation. Second, a greedy algorithm optimizes both the size and the topological features of the network. Not only do achieved results show that this method is able to obtain reliable networks, but also that it significantly improves topological features. Moreover, the usefulness of the method is proven by an application to a human dataset on post-traumatic stress disorder, revealing an innate immunity-mediated response to this pathology. These results are indicative of the method’s potential in the field of biomarkers discovery and characterization. View Full-Text
Keywords: gene networks; scale-free networks; ensemble networks; graph theory; computational biology; mutual information networks; biomarkers discovery gene networks; scale-free networks; ensemble networks; graph theory; computational biology; mutual information networks; biomarkers discovery
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Gómez-Vela, F.; Delgado-Chaves, F.M.; Rodríguez-Baena, D.S.; García-Torres, M.; Divina, F. Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks. Entropy 2019, 21, 1139.

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