Table 1.

Comparison of tools used to predict cancer driver genes.

Method	Data type	Description
20/20	Mutation data	≥20% truncating mutations is TSG; >20% missense mutations in recurrent positions is OCG
Oncodrive Role	Mutation and copy-number alteration data	Machine-learning approach using 30 features related to the pattern of alterations across tumors
ActiveDriver	Mutation data	Detecting cancer drivers based on unexpected mutation sites in phosphorylation regions
e-Driver	Mutation data	Identification of proteins with somatic missense mutations using domain based mutation analysis
MutSig2CV	Mutation and gene expression data	Identification of significantly mutated genes incorporating expression levels and replication times of DNA
DriverNet	Mutation, copy-number alteration, and gene expression data	Method that use interaction networks to identify mutated genes associated with the gene expression alterations of its known interacting genes