Disease-IncRNA associations prediction based on fast random walk with restart in heterogeneous networks

Abstract

Long non-coding RNAs (lncRNAs) represent a fundamental category of epigenetic modulators. Recent research has revealed that lncRNAs play critical roles in gene regulatory mechanisms, substantially influencing the pathogenesis of various human diseases. In this study, a multilayer heterogeneous network was created and we introduced the fast random walk with restart (FRWR) for predicting connections between lncRNAs and diseases. By combining the similarity network of lncRNA, similarity network of disease, and association network of existing lncRNA-disease, a multilayer heterogeneous network was constructed, and the fast random walk with restart method (FRWR) was applied on this network to predict additional potential lncRNA-disease associations. The AUROC value of 0.9034, achieved through leave-one-out cross-validation, underscored the predictive precision of the FRWR technique. Furthermore, a case study of three different diseases provided further validation of the reliability of prediction results. Overall, the multilayer network FRWR method proposed in this work could effectively forecasting the connections between lncRNAs and diseases, offering valuable insights into comprehending the functions of lncRNAs in the context of human health and disease. The source code for the FRWR method can be accessed at: https://github.com/TianTianTian14/FRWR.