Comprehensive evaluation of computational methods for predicting cancer driver genes.

ABSTRACT: Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive evaluation of the performance levels of network-, function- and frequency-based methods is lacking. In the present study, we assessed and compared eight performance criteria for eight network-based, one function-based and three frequency-based algorithms using eight benchmark datasets. Under different conditions, the performance of approaches varied in terms of network, measurement and sample size. The frequency-based driverMAPS and network-based HotNet2 methods showed the best overall performan

SUBMITTER: Shi X 

PROVIDER: S-EPMC8921613 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature