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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM) – Genomics

Open Access Research

A Bayesian decision fusion approach for microRNA target prediction

Dong Yue1, Maozu Guo2, Yidong Chen3 and Yufei Huang13*

Author Affiliations

1 Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas 78249, USA

2 Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

3 Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, USA

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BMC Genomics 2012, 13(Suppl 8):S13  doi:10.1186/1471-2164-13-S8-S13

Published: 17 December 2012

Abstract

MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network. BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm. All the related materials including genome-wide prediction of human targets and a web-based tool are available at http://compgenomics.utsa.edu/gene/gene_1.php webcite.