Publications

@article{Gómez-Vela2015,

title = {Gene network coherence based on prior knowledge using direct and indirect relationships},

author = {F. Gómez-Vela and J. A. Lagares and N. Díaz-Díaz},

url = {http://www.sciencedirect.com/science/article/pii/S1476927115000481},

doi = {10.1016/j.compbiolchem.2015.03.002},

issn = {1476-9271},

year  = {2015},

date = {2015-06-01},

journal = {Computational Biology and Chemistry},

volume = {56},

pages = {142-151},

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.},

keywords = {Biological knowledge, Gene Network},

pubstate = {published},

tppubtype = {article}

}