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SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification

Authors :
Jialuo Xu
Jun Hao
Xingyu Liao
Xuequn Shang
Xingyi Li
Source :
International Journal of Molecular Sciences, Vol 25, Iss 19, p 10351 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used graph deep learning methods to identify cancer driver genes based on biological networks. However, incompleteness and the noise of the networks will weaken the performance of models. To address this, we propose a cancer driver gene identification method based on self-supervision for graph convolutional networks, which can efficiently enhance the structure of the network and further improve predictive accuracy. The reliability of SSCI is verified by the area under the receiver operating characteristic curves (AUROC), the area under the precision-recall curves (AUPRC), and the F1 score, with respective values of 0.966, 0.964, and 0.913. The results show that our method can identify cancer driver genes with strong discriminative power and biological interpretability.

Details

Language :
English
ISSN :
14220067 and 16616596
Volume :
25
Issue :
19
Database :
Directory of Open Access Journals
Journal :
International Journal of Molecular Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.8f0f88b015984371bb0ed4e5427584a7
Document Type :
article
Full Text :
https://doi.org/10.3390/ijms251910351