Predicting multiple types of MicroRNA-disease associations based on tensor factorization and label propagation

Abstract

MicroRNAs (miRNAs) play important regulatory roles in the pathogenesis and progression of diseases. Most existing bioinformatics methods only study miRNA-disease binary association prediction. However, there are many types of associations between miRNA and disease. In addition, the miRNA-disease-type association dataset has inherent noise and incompleteness. In this paper, a novel method based on tensor factorization and label propagation (TFLP) is proposed to alleviate the above problems. First, as an effective tensor factorization method, tensor robust principal component analysis (TRPCA) is applied to the original multiple-type miRNA-disease associations to obtain a clean and complete low-rank prediction tensor. Second, the Gaussian interaction profile (GIP) kernel is used to describe the similarity of disease pairs and the similarity of miRNA pairs. Then, they are combined with disease semantic similarity and miRNA functional similarity to obtain an integrated disease similarity network and an integrated miRNA similarity network, respectively. Finally, the low-rank association tensor and the biological similarity as auxiliary information are introduced into label propagation. The prediction performance of the algorithm is improved by iterative propagation of labeled information to unlabeled samples. Extensive experiments reveal that the proposed TFLP method outperforms other state-of-the-art methods for predicting multiple types of miRNA-disease associations. The data and source codes are available at https://github.com/nayu0419/TFLP.