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We used the LeMoNe algorithm to infer the module network [11], [12], [54].
In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques.
As a regulator can be associated with more than one module, the ensemble forms a module network.
Systems approaches, like the inference of a module network from expression data, can help to achieve this goal.
Those results corroborate module network inference as a robust and useful approach to gain more precise insights into miRNA function.
During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms.
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By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network.
This inter-module network allowed us to mine the drug action pattern by incorporating drug-gene relationships.
Impressively, in the intrinsic inter-module network, the gene modules 'UBQLN4 (ubiquilin 4) subnetwork' shared synergy with 360 gene modules (Figure 2b).
Finally, by projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a 'perturbation index' to quantify the potential efficacy of drugs in terms of the drugs' perturbation pattern on the inter-module network (see Methods).
To characterize the intrinsic features of an inter-module network, particularly the identification of 'gatekeeper modules', we further compared the rates of genetic (somatic mutation) and epigenetic (DNA methylation) aberration on tumor vs. normal tissues.
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