HGCMLDA: predicting lncRNA-disease associations using hypergraph contrastive learning and multi-scale attentional feature fusion.

ABSTRACT: Research has consistently indicated that long non-coding RNAs (lncRNAs) significantly influence the development of numerous diseases. Predicting lncRNA-disease associations (LDAs) will contribute to the prevention and treatment of diseases. However, most existing computational models suffer from several challenges: (i) difficulty in capturing complex higher-order relationships among nodes; (ii) limited number of known associations and neglect of consistency of representations across views; (iii) inadequate fusion of multi-view data. In this research, we introduce an innovative end-to-end method named HGCMLDA for LDA prediction. Firstly, HGCMLDA constructs hypergraphs of lncRNAs and diseases based on integrated similarity matrices utilizing Gaussian mixture model and k-nearest neighbor meth

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12152417 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature