Learning Large-Scale Graphical Gaussian Models from Genomic Data

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… (More)

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