De novo identification of maximally deregulated subnetworks based on multi-omics data with deregnet

HIGHLIGHTS

SUMMARY

    A lot of biomolecular interactions are directed in nature, e_g protein A phosphorylates protein B, enzyme A precedes enzyme B in a metabolic pathway in contrast to symmetric interactions such as physical interactions of proteins in protein complexes. The authors show that the algorithm, DeRegNet, can be interpreted as maximum_likelihood estimation under a certain natural statistical model. The authors introduce a personalized approach to interpreting cancer data and introduce the notion of network-defined cancer genes which allow to identify patient groups based on their similarity of their detected personalized subgraphs. Comparison of node . . .

     

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