Abstract

The inference and modeling of network-like structures in genomic data is of prime importance in systems biology. Complex stochastic associations and interdependencies can very generally be described as a graphical model. However, the paucity of available samples in current highthroughput experiments renders learning graphical models from genome data, such as microarray expression profiles, a challenging and very hard problem. Here we review several recently developed 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.

Citations