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Again, although Multi-Dendrix identifies multiple driver pathways by maximizing the sum of sample coverage of each driver pathway, it does not require these pathways to be mutated in the same set of samples.
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However, our results can be generalized to the case of multiple disjoint driver pathways.
It is also possible that multiple driver mutations are necessary to perturb a pathway and thus these mutations co-occur in patients.
The identification of mutated driver genes and driver pathways from these data is a significant challenge.
There are two hypotheses when identifying collaborative driver pathways.
For instance, J. Shang et al. evaluated and compared multiple aligners for next-generation sequencing data, providing an important guide for biologists to select suitable aligners, and H. Li et al. proposed a method to identify mutated driver pathways in cancer.
MUDPAC identifies collaborative driver pathways in cancer using a two-step approach: mutational pathway enrichment analysis followed by greedy search for the collaborative driver pathways.
There are several recent methods that use additional information to help predict driver genes and driver pathways.
In both models driver mutations occur in sets of genes, which we refer to as driver pathways.
As a result, very few pathways may be identified as the set of driver pathways.
In particular the following iterative procedure identifies all driver pathways using our algorithms: after identifying a driver pathway, remove its genes from the mutation matrix, and look for driver pathways in the reduced mutation matrix.
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