TY - JOUR AU - Nono, Alice Djotsa AU - Chen, Ken AU - Liu, Xiaoming PY - 2019 DA - 2019/01/31 TI - Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes JO - BMC Medical Genomics SP - 22 VL - 12 IS - 1 AB - 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 functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage. SN - 1755-8794 UR - https://doi.org/10.1186/s12920-018-0452-9 DO - 10.1186/s12920-018-0452-9 ID - Nono2019 ER -