Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes.

ABSTRACT:

Background

Identifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of f

SUBMITTER: Nono AD 

PROVIDER: S-EPMC6357357 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature