Research Article Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MicroRNAs constitute an important class of noncoding, single-stranded, ∼22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a fewmicroRNA-disease associations have been confirmed by traditional experimental approaches.We introduce twomethods to predictmicroRNA-disease association.Thefirstmethod, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods. 1