GASN: gamma distribution test for driver genes identification based on similarity networks

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.

KEYWORDS:

• Cancer
• similarity networks
• function impact scores
• driver genes
• machine learning

Acknowledgments

The authors would like to thank anonymous reviewers for their very detailed and helpful reviews. Dazhi Jiang: Conceptualization, Programming and writing. Runguo Wei: Conceptualization, Methodology, Writing and proofreading. Zhihui He: Programming and proofreading. Cheng Liu: Methodology, Writing and proofreading. Senlin Lin: Writing and proofreading. Yingqing Lin: Conceptualization, Methodology, Writing and proofreading.

Disclosure statement

No potential conflict of interest was reported by the authors

Additional information

Funding

This work is supported by National Natural Science Foundation of China (62106136, 61902231), Natural Science Foundation of Guangdong Province (2019A1515010943), Key Project of Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Natural Science) (2018KZDXM035), The Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Special Projects in Artificial Intelligence) (2019KZDZX1030) and 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (2020LKSFG04D), and Science and Technology Major Project of Guangdong Province (STKJ2021005).