In:
Journal of Bioinformatics and Computational Biology, World Scientific Pub Co Pte Ltd, Vol. 13, No. 04 ( 2015-08), p. 1550014-
Abstract:
Identifying the microRNA–disease relationship is vital for investigating the pathogenesis of various diseases. However, experimental verification of disease-related microRNAs remains considerable challenge to many researchers, particularly for the fact that numerous new microRNAs are discovered every year. As such, development of computational methods for disease-related microRNA prediction has recently gained eminent attention. In this paper, first, we construct a miRNA functional network and a disease similarity network by integrating different information sources. Then, we further introduce a new diffusion-based method (NDBM) to explore global network similarity for miRNA–disease association inference. Even though known miRNA–disease associations in the database are rare, NDBM still achieves an area under the ROC curve (AUC) of 85.62% in the leave-one-out cross-validation in improving the prediction accuracy of previous methods significantly. Moreover, our method is applicable to diseases with no known related miRNAs as well as new miRNAs with unknown target diseases. Some associations who strongly predicted by our method are confirmed by public databases. These superior performances suggest that NDBM could be an effective and important tool for biomedical research.
Type of Medium:
Online Resource
ISSN:
0219-7200
,
1757-6334
DOI:
10.1142/S0219720015500146
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2015
SSG:
12
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