RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method.

ABSTRACT: While gene fusions have been increasingly detected by next-generation sequencing (NGS) technologies based methods in human cancers, these methods have limitations in identifying driver fusions. In addition, the existing methods to identify driver gene fusions ignored the specificity among different cancers or only considered their local rather than global topology features in networks. Here, we proposed a novel network-based method, called RWCFusion, to identify phenotype-specific cancer driver gene fusions. To evaluate its performance, we used leave-one-out cross-validation in 35 cancers and achieved a high AUC value 0.925 for overall cancers and an average 0.929 for signal cancer. Furthermore, we classified 35 cancers into two classes: haematological and solid, of which the haematologica

SUBMITTER: Zhao J 

PROVIDER: S-EPMC5308635 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature