Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced
algorithms to identify drivers now exist, but systematic attempts to combine and optimize …

The prevalence of neutral mutations in cancer cell population impedes the distinguishing of
cancer-causing driver mutations from passenger mutations. To systematically prioritize the …

Somatic mutations in cancer genomes include drivers that provide selective advantages to
tumor cells and passengers present due to genome instability. Discovery of pan-cancer …

With the ability to fully sequence tumor genomes/exomes, the quest for cancer driver genes
can now be undertaken in an unbiased manner. However, obtaining a complete catalog of …

Large-scale cancer sequencing studies of patient cohorts have statistically implicated many
genes driving cancer growth and progression, and their identification has yielded substantial …

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver
mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis …

Although rapid progress has been made in computational approaches for prioritizing cancer
driver genes, research is far from achieving the ultimate goal of discovering a complete …

Cancer genomes contain large numbers of somatic mutations but few of these mutations
drive tumor development. Current approaches either identify driver genes on the basis of …

Motivation: Most approaches used to identify cancer driver genes focus, true to their name,
on entire genes and assume that a gene, treated as one entity, has a specific role in cancer …

We previously presented DriverDB, a database that incorporates∼ 6000 cases of exome-
seq data, in addition to annotation databases and published bioinformatics algorithms …