Gene network coherence based on prior knowledge using direct and indirect relationships. (June 2015)

• Record Type: Journal Article
• Authors: Gómez-Vela, Francisco; Lagares, José Antonio; Díaz-Díaz, Norberto
• Is Part Of: Computational biology and chemistry. Volume 56(2015)
• Page Start: 142
• Page End: 151
• Publication Date: 2015-06
• Subjects: Gene association networks -- Biological knowledge -- Gene network assessment -- Biological validation -- Heuristic algorithm; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.03.002
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract : Graphical abstract: Abstract : Highlights: GNC is a novel approach for assessing gene association networks. GNC considers not only the direct relationships, but also the indirect ones. A ROC analysis shows that GNC outperforms other measures like PPV or F -measures. GNC also presents a sensitive behavior against the effect of noise. GNC is a robust solution to the problem of gene network biological validation. Abstract: Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene–gene interactions for validation, leaving aside the weak (indirect) relationships. We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene–gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at:http://fgomezvela.github.io/GNC/ . The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene–gene relationships, offers a new robust solution to the problem of GNs biological validation.