The construction and the understanding of Gene Regulatory Networks (GRNs) are among the hardest tasks faced by systems biology. The inference of a GRN from gene expression data (the GRN reverse engineering), is a challenging task that requires the exploitation of diverse mathematical and computational techniques. The DREAM conference proposes several challenges about the inference of biological networks and/or the prediction of how they are influenced by perturbations. This paper describes a method for GRN reverse engineering that the authors submitted to the 2010 DREAM challenge. The methodology is based on a combination of well known statistical methods into a Naive Bayes classifier. Despite its simplicity the approach fared fairly well when compared to other proposals on real networks.

Tackling the dream challenge for gene regulatory networks reverse engineering

VISCONTI, ALESSIA;ESPOSITO, Roberto;CORDERO, Francesca
2011

Abstract

The construction and the understanding of Gene Regulatory Networks (GRNs) are among the hardest tasks faced by systems biology. The inference of a GRN from gene expression data (the GRN reverse engineering), is a challenging task that requires the exploitation of diverse mathematical and computational techniques. The DREAM conference proposes several challenges about the inference of biological networks and/or the prediction of how they are influenced by perturbations. This paper describes a method for GRN reverse engineering that the authors submitted to the 2010 DREAM challenge. The methodology is based on a combination of well known statistical methods into a Naive Bayes classifier. Despite its simplicity the approach fared fairly well when compared to other proposals on real networks.
XIIth International Conference of the Italian Association for Artificial Intelligence
Palermo
September 15-17, 2011
6934
372
382
http://www.springer.com/computer/ai/book/978-3-642-23953-3
Alessia Visconti; Roberto Esposito; Francesca Cordero
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/89051
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