Enumeration of Likely Gene Networks and Network Motif Extraction for Large Gene Networks

Abstract

The reliable estimation of gene networks from gene expression measurements is a major challenge in the field of Bioinformatics. Recently, an algorithm for the optimal estimation of small gene networks within the Bayesian network framework was found [3]. This algorithm was further extended to allow the enumeration of all optimal networks and also suboptimal networks in the order of their likelihood [2]. In this work, we show how this result can be applied to the enumeration of likely gene networks for a large number of genes. Enumerating a number of the most likely gene network models instead of just focusing on the single most likely network model allows to evaluate the reliability of the estimations. If we can find a partial network that is common to most of the likely network models, we can expect this part to be the most reliable part. We denote such common parts as gene network motifs.1