Data fusion-based algorithm for predicting miRNA–Disease associations

Technological progress and the development of laboratory techniques and bioinformatics tools have led to the availability of ever-increasing amounts of biological data including genomic, proteomic, and transcriptomic sequences and related information. These data have helped in understanding some of the complicated life process from a systematic level. Many diseases are generated by abnormalities in multiple regulating processes. In this study, we constructed a novel miRNA–gene–disease fusion (MGDF) algorithm by integrating three genome-wide networks, namely microRNA (miRNA), gene function, and disease similarity networks. The data fusion method was applied to construct a miRNA–gene–disease association network model from these networks to explore miRNA–disease associations mediated by genes with similar functions. mmiRNAs bind to their target genes and regulate their expression, so the miRNA–gene and gene–disease regulatory relationships were included in the network model to more accurately predict miRNA–disease associations. The proposed MGDF was used to predict miRNA–cancer associations and the results show that most of the predicted associations had evidence in existing databases.