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MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules
(Bioinformatics, 2019)
Motivation: Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem
which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem ...
Predicting clinical outcomes in neuroblastoma with genomic data integration
(Biology Direct, 2018)
Background: Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected ...
DriveWays: a method for identifying possibly overlapping driver pathways in cancer
(Nature Research, 2020)
The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly ...