TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network

Abstract

Many microRNAs (miRNAs) have been confirmed to be associated with the generation of human diseases. Capturing miRNA–disease associations (M-DAs) provides an effective way to understand the etiology of diseases. Many models for predicting M-DAs have been constructed; nevertheless, there are still several limitations, such as generally considering direct information between miRNAs and diseases, usually ignoring potential knowledge hidden in isolated miRNAs or diseases. To overcome these limitations, in this study a novel method for predicting M-DAs was developed named TLNPMD, highlights of which are the introduction of drug heuristic information and a bipartite network reconstruction strategy. Specifically, three bipartite networks, including drug–miRNA, drug–disease, and miRNA–disease, were reconstructed as weighted ones using such reconstruction strategy. Based on these weighted bipartite networks, as well as three corresponding similarity networks of drugs, miRNAs and diseases, the miRNA–drug-disease three-layer heterogeneous network was constructed. Then, this heterogeneous network was converted into three two-layer heterogeneous networks, for each of which the network path computational model was employed to predict association scores. Finally, both direct and indirect miRNA–disease paths were used to predict M-DAs. Comparative experiments of TLNPMD and other four models were performed and evaluated by five-fold and global leave-one-out cross validations, results of which show that TLNPMD has the highest AUC values among those of compared methods. In addition, case studies of two common diseases were carried out to validate the effectiveness of the TLNPMD. These experiments demonstrate that the TLNPMD may serve as a promising alternative to existing methods for predicting M-DAs.

CircRNA-disease inference using deep ensemble model based on triple association

“…After obtaining all the similarity and association matrix, researchers implemented network-based algorithms such as Random Walk with Restart [16] or KATZ method [19] to get predicted disease-related miRNAs/lncRNAs [20]. There are other recent deep learning based methods like TLNPMD [21], MINIMIDA [22], etc. that utilized convolutional network for feature extraction and association inference, but they suffer from high computational cost due to large trainable parameters.…”