A Semi-Supervised Learning Algorithm for Predicting MiRNA-Disease Association

The dysregulation of miRNAs can lead to disease. Modeling miRNA-disease association is important in understanding the pathogenesis of diseases. The generation of multiple types of miRNA-disease association data provides us an opportunity to in-depth study of the interaction mechanism between miRNA and disease. However, the existing methods usually only focus on miRNA-disease interaction, which ignores the relationship types of their association. In this paper, we studied the feasibility of tensor robust principal component analysis (TRPCA) to model miRNA-disease-type associations. Unlike the binary association of , we use the triple association of to better describe the pathogenesis of the disease. Multi-view auxiliary information (semantics of diseases, functions of miRNAs and Gaussian interaction profile kernel) are applied to fully consider the complexity of biological processes. The proposed semi-supervised learning model combines TRPCA and label propagation. The 10-fold cross validation (CV) and case study experiments on HMDD v2.0 dataset demonstrate that the proposed model is superior to the other benchmark methods.