Edge Selection in a Noisy Graph by Concept Analysis: Application to a Genomic Network

Abstract

MicroRNAs (miRNAs) are small RNA molecules that bind messenger RNAs (mRNAs) to silence their expression. Understanding this regulation mechanism requires the study of the miRNA/mRNA interaction network. State-of-the-art methods for predicting interactions lead to a high level of false positive: the interaction score distribution may be roughly described as a mixture of two overlapping Gaussian laws that need to be discriminated with a threshold. In order to further improve the discrimination between true and false interactions, we present a method that considers the structure of the underlying graph. We assume that the graph is formed on a relatively simple structure of formal concepts (associated with regulation modules in the regulation mechanism). Specifically, the formal context topology of true edges is assumed to be less complex than in the case of a noisy graph including spurious interactions or missing interactions. Our approach consists thus in selecting edges below an edge score threshold and applying a repair process on the graph, adding or deleting edges to decrease the global concept complexity. To validate our hypothesis and method, we have extracted parameters from a real biological miRNA/mRNA network and used them to build random networks with fixed concept topology and true/false interaction ratio. Each repaired network can be evaluated with a score balancing the number of edge changes and the conceptual adequacy in the spirit of the minimum description length principle.