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Publication - Dr Pau Erola

    Learning Differential Module Networks Across Multiple Experimental Conditions


    Erola, P, Bonnet, E & Michoel, T, 2019, ‘Learning Differential Module Networks Across Multiple Experimental Conditions’. in: Gene Regulatory Networks., pp. 303-321


    Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.

    Full details in the University publications repository