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

Abstract EN

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.