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. 2015:2015:810514.
doi: 10.1155/2015/810514. Epub 2015 Jul 26.

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

Quan Zou  1 , Jinjin Li  2 , Qingqi Hong  3 , Ziyu Lin  2 , Yun Wu  4 , Hua Shi  4 , Ying Ju  2
Affiliations

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

Quan Zou et al. Biomed Res Int. 2015.

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.

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Figures

Figure 1
Figure 1
Bipartite graph of the microRNA-disease association network.
Figure 2
Figure 2
Degree distributions of microRNAs and diseases in the bipartite graph of the microRNA-disease association network.
Figure 3
Figure 3
Unweighted, undirected graph.
Figure 4
Figure 4
ROC curves of KATZ and CATAPULT methods by leave-one-out cross-validation.
Figure 5
Figure 5
ROC curves of KATZ and CATAPULT methods by 3-fold cross-validation.
Figure 6
Figure 6
Recovery of microRNA-disease associations with respect to disease rank under leave-one-out cross-validation.
See this image and copyright information in PMC

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