GASN: gamma distribution test for driver genes identification based on similarity networks. Issue 1 (31st December 2023) - Jiang, Dazhi; Wei, Runguo; He, Zhihui; Lin, Senlin; Liu, Cheng; Lin, Yingqing
ABSTRACT: Cancer is a disease with a complex genome of altered functions. However, most existing driver gene identification approaches rarely consider driver genes may have the same functional properties. To overcome this issue, we propose the gamma distribution test for the driver gene identification based on similarity networks, termed GASN, which identifies driver genes by combining machine learning and distributional statistics methods. Similarity networks are able to learn gene similarities and key features that represent the functional impact of genes. In addition, we classify genes into different cellular compartments and use the gamma distribution test within cellular compartments to identify significant driver genes. The experimental results show that our method outperforms the other 17 comparative methods.