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

Journal: BioMed research international
Published Date:

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.

Authors

  • Quan Zou
  • Jinjin Li
    Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China.
  • Qingqi Hong
    Software School, Xiamen University, Xiamen 361005, China.
  • Ziyu Lin
    School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
  • Yun Wu
    Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA.
  • Hua Shi
    School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China.
  • Ying Ju
    School of Information Science and Engineering, Xiamen University, Xiamen, China.