Integrative Approaches for Predicting microRNA Function and Prioritizing Disease-Related microRNA Using Biological Interaction Networks

Abstract

MicroRNAs (miRNAs) play critical roles in regulating gene expressions at post-transcriptional levels. The prediction of disease-related miRNA is vital to the further investigation of miRNA’s involvement in the pathogenesis of a disease. In previous years, biological experimentation was the main method used to identify whether miRNA is associated with a given disease. With increasing biological information and the appearance of new miRNAs every year, experimental identification of disease-related miRNAs poses considerable difficulties (e.g. time consumption and high cost). Because of the experimental methods’ limitations in determining the relationship between miRNAs and diseases, computational methods have been proposed. A key to predict potential disease-related miRNA based on networks is the calculation of similarity among diseases and miRNA over the networks. Different strategies lead to different results. In this chapter, we summarize the existing computational approaches and present the confronted difficulties that help understand the research status. We also discuss the principles, efficiency and differences among these methods. The comprehensive comparison and discussion elucidated in this work provide constructive insights into the matter.