Learning large-scale graphical gaussian models from genomic data

Abstract. The inference and modeling of network-like structures in genomic data is of prime im-portance in systems biology. Complex stochastic associations and interdependencies can very gen-erally be described as a graphical model. However, the paucity of available samples in current high-throughput experiments renders learning graphical models from genome data, such as microarray expression profiles, a challenging and very hard problem. Here we review several recently devel-oped approaches to small-sample inference of graphical Gaussian modeling and discuss strategies to cope with the high dimensionality of functional genomics data. Particular emphasis is put on regularization methods and an empirical Bayes network inference procedure