From digital genetics to knowledge discovery: Perspectives in genetic network understanding

Abstract

In this paper, we propose an original computational approach to assist knowledge discovery in complex biological networks. First, we present an integrated model of the evolution of regulation networks that can be used to uncover organization principles of such networks. Then, we propose to use the results of our model as a benchmark for knowledge discovery algorithms. We describe a first experiment of such benchmarking by using gene knock-out data generated from the modeled organisms.