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

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 o...

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Bibliographic Details
Published inConnection science Vol. 35; no. 1
Main Authors Jiang, Dazhi, Wei, Runguo, He, Zhihui, Lin, Senlin, Liu, Cheng, Lin, Yingqing
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 31.12.2023
Taylor & Francis Ltd
Subjects
Cancer
Compartments
driver genes
function impact scores
Genes
Machine learning
Networks
Probability distribution functions
Similarity
similarity networks
Statistical methods
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Summary: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.
ISSN:0954-0091
1360-0494
DOI:10.1080/09540091.2023.2167937