GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association

Lei Wang, Zheng Wei Li, Zhu Hong You, De Shuang Huang, Leon Wong

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Growing studies reveal that Circular RNAs (circRNAs) are broadly engaged in physiological processes of cell proliferation, differentiation, aging, apoptosis, and are closely associated with the pathogenesis of numerous diseases. Clarification of the correlation among diseases and circRNAs is of great clinical importance to provide new therapeutic strategies for complex diseases. However, previous circRNA-disease association prediction methods rely excessively on the graph network, and the model performance is dramatically reduced when noisy connections occur in the graph structure. To address this problem, this paper proposes an unsupervised deep graph structure learning method GSLCDA to predict potential CDAs. Concretely, we first integrate circRNA and disease multi-source data to constitute the CDA heterogeneous network. Then the network topology is learned using the graph structure, and the original graph is enhanced in an unsupervised manner by maximize the inter information of the learned and original graphs to uncover their essential features. Finally, graph space sensitive k-nearest neighbor (KNN) algorithm is employed to search for latent CDAs. In the benchmark dataset, GSLCDA obtained 92.67% accuracy with 0.9279 AUC. GSLCDA also exhibits exceptional performance on independent datasets. Furthermore, 14, 12 and 14 of the top 16 circRNAs with the most points GSLCDA prediction scores were confirmed in the relevant literature in the breast cancer, colorectal cancer and lung cancer case studies, respectively. Such results demonstrated that GSLCDA can validly reveal underlying CDA and offer new perspectives for the diagnosis and therapy of complex human diseases.

Original languageEnglish
Pages (from-to)1742-1751
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume28
Issue number3
DOIs
StatePublished - 1 Mar 2024

Keywords

  • CircRNA
  • CircRNA-disease association (CDA)
  • graph structure learning
  • multi-source information heterogeneous network

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