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These same studies and others have also begun to explore the clinical relevance of such model signatures by inferring pathway activity across human tumours and correlating the inferred patterns with clinical variables [ 1, 6, 7, 9- 14].
Oncogenic signatures observed experimentally have potential use for inferring pathway deregulation in human tumors.
Previous network-based methods suffer less from incomplete gene annotations for inferring pathway activity; they also have difficulty in analyzing pathway activity across cancers.
While statistical methods for inferring pathway activation levels from corresponding model signatures have been proposed [ 2, 5, 6, 27- 29], it has recently become clear that model signatures exhibit a highly complex modular structure that needs to be factored in when estimating pathway activity [ 5].
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The present approach (GASA) was motivated by the inference of genetic interactions, which have potential for inferring pathways in yeast [ 3, 4].
As a result, it may be useful in inferring pathways underlying stochastic processes occurring over much shorter timescales, such as disease or tumour progression, or the differentiation of cell types.
For instance, in [ 110] the C3NET inference method [ 116, 117] has been used to infer pathway specific networks for prostate cancer.
The maximum a posteriori values of the parameters, learned by a message-passing belief propagation procedure, can be used to explain observed data and infer pathway memberships [9], [10].
We estimated inferred pathway levels (IPL) for each entity in superPathway at the individual patient level.
Additionally, the DACS Network incorporates transcriptomic data through the integration of computationally derived nodes to infer pathway activity.
In contrast, we use the differentially expressed genes in the context of prior knowledge-derived causal relationships between the genes and their upstream controllers to infer pathway activity.
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