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

Jialuo Xu, Jun Hao, Xingyu Liao, Xuequn Shang, Xingyi Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Article number10351
JournalInternational Journal of Molecular Sciences
Volume25
Issue number19
DOIs
StatePublished - Oct 2024

Keywords

  • cancer driver genes
  • graph learning
  • network structure enhancement
  • self-supervised deep learning

Fingerprint

Dive into the research topics of 'SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification'. Together they form a unique fingerprint.

Cite this