Journal
BRIEFINGS IN BIOINFORMATICS
Volume 17, Issue 2, Pages 193-203Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbv033
Keywords
disease miRNA prediction; network similarity; biological database
Funding
- Natural Science Foundation of China [61370010, 61371179]
- Natural Science Foundation of Fujian Province of China [2014J01253]
- Shanghai Key Laboratory of Intelligent Information Processing, China [IIPL-2014-004]
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MicroRNAs (miRNA) play critical roles in regulating gene expressions at the posttranscriptional levels. The prediction of disease-related miRNA is vital to the further investigation of miRNA's involvement in the pathogenesis of disease. In previous years, biological experimentation is the main method used to identify whether miRNA was associated with a given disease. With increasing biological information and the appearance of new miRNAs every year, experimental identification of disease-related miRNAs poses considerable difficulties (e.g. time-consumption and high cost). Because of the limitations of experimental methods in determining the relationship between miRNAs and diseases, computational methods have been proposed. A key to predict potential disease-related miRNA based on networks is the calculation of similarity among diseases and miRNA over the networks. Different strategies lead to different results. In this review, we summarize the existing computational approaches and present the confronted difficulties that help understand the research status. We also discuss the principles, efficiency and differences among these methods. The comprehensive comparison and discussion elucidated in this work provide constructive insights into the matter.
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