GASN: gamma distribution test for driver genes identification based on similarity networks. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Jiang, Dazhi; Wei, Runguo; He, Zhihui; Lin, Senlin; Liu, Cheng; Lin, Yingqing
• Is Part Of: Connection science. Volume 35:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Cancer -- similarity networks -- function impact scores -- driver genes -- machine learning; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2023.2167937
• Languages: English
• ISSNs: 0954-0091

Abstract

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.