Inferring potential associations between microRNAs (miRNAs) and human diseases can help people understand the pathogenesis of complex human diseases. Several computational approaches have been presented to discover novel miRNA-disease associations based on a top-ranked association model. However, some top-ranked miRNAs are not easily used to reveal the association between miRNAs and diseases. This study aims to infer miRNA-disease relationship by identifying a functional module. We first construct a miRNA functional similarity network derived from a disease similarity network and a known miRNA-disease relationship network. We then present an improved K-means (i.e., IK-means) algorithm to detect miRNA functional modules and used 243 diseases to validate the performance of our proposed method. Experimental results indicate that the performance of IK-means is better compared with classical K-means algorithms. Case studies on some functional modules further demonstrate the applicability of IK-means in the identification of new miRNA-disease associations.
Keywords: clustering; functional module; functional similarity network; miRNA similarity; microRNA-disease association.