Abstract:
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Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGMs to incorporate PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we developed an augmented high-dimensional graphical lasso (AhGlasso) model to incorporate edge weights in known PPI with omics data for global network learning. This new method outperforms weighted graphical lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. Using proteomic data from a study on chronic obstructive pulmonary disease, we also demonstrate how AhGlasso improves protein network inference by incorporating PPI information.
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