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

Quan Zou,Ying Ju,Yun Wu,Jinjin Li,Qingqi Hong,Ziyu Lin,Hua Shi

doi:10.1155/2015/810514

Abstract

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.

Highlights

MicroRNAs constitute a class of non-protein-coding small RNAs, 20 to 25 nucleotides long, that bind to the 3󸀠 untranslated region of target mRNAs to regulate mRNA turnover and translation
The KATZ and CATAPULT methods were applied to the 242 known microRNA-disease associations to infer potential microRNA-disease associations
The set of 242 known microRNA-disease associations is regarded as the “gold standard” data and was used to evaluate the performance of KATZ and CATAPULT methods in the leaveone-out and 3-fold cross-validation experiment and training dataset in the comprehensive prediction [62]

Summary

Introduction

MicroRNAs constitute a class of non-protein-coding small RNAs, 20 to 25 nucleotides long, that bind to the 3󸀠 untranslated region of target mRNAs to regulate mRNA turnover and translation. Many studies have found that microRNAs play an important role in cellular signaling networks [4], tissue development, [5,6,7] and cell growth [8]. They are associated with various diseases [9, 10], including breast cancer [11, 12], lung cancer [13, 14], cardiomyopathy [15], and cell lymphoma [16]. Dysregulation of microRNAs can affect apoptosis signaling pathways and cell cycle regulation in cancer [18]

Objectives

Methods

Results

Conclusion