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Paper: "Network deconvolution as a general method to distinguish direct dependencies in networks".
In contrast, the multivariate approach of PDC is suited to characterize solely direct dependencies of two signals under study.
The main reason is that most correlations represent indirect dependencies instead of direct dependencies.
On the other hand, the indirect dependency is caused by direct dependencies through some intermediate nodes.
However, in memory, only direct dependencies are modeled.
Biologically this allows to include direct dependencies on multiple mutations.
By parsing the direct dependencies stored in memory, an unrolled dependency graph as shown in Fig. 10 is constructed.
Then the direct dependencies between the target gene and the other genes are extracted and stored.
Network deconvolution (ND) [ 11] is a technique to infer direct dependencies among variables.
Currently, the direct dependencies between the different property nodes are defined manually.
Here, neighbors have direct dependencies, since indirect gene connections are removed by this method.
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