Nested q-Partial Graphs for Genetic Network Inference from "Small n, Large p" Microarray Data

Abstract. Gaussian graphical models are widely used to tackle the important and challenging problem of inferring genetic regulatory networks from expression data. These models have gained much attention as they encode full conditional relationships between variables, i.e. genes. Unfortunately, microarray data are characterized by a low number of samples compared to the number of genes. Hence, classical approaches to estimate the full joint distribution cannot be applied. Recently, limitedorder partial correlation approaches have been proposed to circumvent this problem. It has been shown both theoretically and experimentally that such graphs provide accurate approximations of the full conditional independence structure between the variables thanks to the sparsity of genetic networks. Alas, computing limited-order partial correlation coefficients for large networks, even for small order values, is computationally expensive, and often even intractable. Moreover, problems deriving from mu...

• Type: Conference
• Year: 2008
• Where: BIRD
• Authors: Kevin Kontos, Gianluca Bontempi
• Keywords: Bioinformatics, BIRD 2008, Limited-order Partial Correlation, Partial Correlation, Partial Correlation Approaches