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MOTIVATION Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of(More)
Constraint-based learning of Bayesian networks (BN) from limited data can lead to multiple testing problems when recovering dense areas of the skeleton and to conflicting results in the orientation of edges. In this paper, we present a new constraint-based algorithm, light mutual min (LMM) for improved accuracy of BN learning from small sample data. LMM(More)
In this section, we consider some of the characteristics of partial correlations between directly dependent variables when conditioning on a large set of true neighbors of either variable. This material is complementary to the discussion of Section 5 (main paper). The considered characteristics are summarized in Conjecture 1. Here, we only provide a proof(More)
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