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

• DOI: 10.1155/2015/810514
• Publication Date: 2015-07-26T17:02:49Z
• Authors: Quan Zou; Jinjin Li; Qingqi Hong; Ziyu Lin; Yun Wu; Hua Shi; Ying Ju
• Keywords: 0301 basic medicine; Models, Genetic; Pattern Recognition, Automated; Social Networking; Machine Learning; MicroRNAs; 03 medical and health sciences; Databases, Genetic; Humans; Computer Simulation; Genetic Predisposition to Disease; Algorithms; Research Article; Signal Transduction

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 few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, 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.