Search

With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA–disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA–disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D 5430 and D 6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA–disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future. [ABSTRACT FROM AUTHOR]

Since miRNAs can participate in the posttranscriptional regulation of gene expression, they may provide ideas for the development of new drugs or become new biomarkers for drug targets or disease diagnosis. In this work, we propose an miRNA–disease association prediction method based on meta-paths (MDPBMP). First, an miRNA–disease–gene heterogeneous information network was constructed, and seven symmetrical meta-paths were defined according to different semantics. After constructing the initial feature vector for the node, the vector information carried by all nodes on the meta-path instance is extracted and aggregated to update the feature vector of the starting node. Then, the vector information obtained by the nodes on different meta-paths is aggregated. Finally, miRNA and disease embedding feature vectors are used to calculate their associated scores. Compared with the other methods, MDPBMP obtained the highest AUC value of 0.9214. Among the top 50 predicted miRNAs for lung neoplasms, esophageal neoplasms, colon neoplasms and breast neoplasms, 49, 48, 49 and 50 have been verified. Furthermore, for breast neoplasms, we deleted all the known associations between breast neoplasms and miRNAs from the training set. These results also show that for new diseases without known related miRNA information, our model can predict their potential miRNAs. Code and data are available at https://github.com/LiangYu-Xidian/MDPBMP. [ABSTRACT FROM AUTHOR]