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

• Main Authors: Quan Zou, Jinjin Li, Qingqi Hong, Ziyu Lin, Yun Wu, Hua Shi, Ying Ju
• Format: Article
• Language: English
• Published: Hindawi Limited 2015-01-01
• Series: BioMed Research International
• Online Access: http://dx.doi.org/10.1155/2015/810514

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.