Using graph theory to analyze gene network coherence

Motivation and Objectives Gene networks (GNs) have become one of the most important approaches for modelling genegene relationships in Bioinformatics (Hecker et al, 2009). These networks allow us to carry out studies of different biological processes in a visual way. Many GN inference algorithms have been developed as techniques for extracting biological knowledge (Ponzoni et al, 2007; Gallo et al, 2011). Once the network has been generated, it is very important to assure network reliability in order to illustrate the quality of the generated model. The quality of a GN can be measured by a direct comparison between the obtained GN and prior biological knowledge (Wei and Li, 2007; Zhou and Wong, 2011). However, these both approaches are not entirely accurate as they only take direct gene–gene interactions into account for the validation task, leaving aside the weak (indirect) relationships (Poyatos, 2011). In this work the authors present a new methodology to assess the biological coherence of a GN. This coherence is obtained according to different biological gene-gene relationships sources. Our proposal is able to perform a complete functional analysis of the input GN. With this aim, graph theory is used to consider not only direct relationships but indirect ones as well.