A Bayesian decision fusion approach for microRNA target prediction

Authors:
Dong Yue;Hui Liu;MingZhu Lu;Philip Chen;Yidong Chen;Yufei Huang
Affiliations:
University of Texas at San Antonio;China University of Mining and Technology, China;University of Texas at San Antonio;University of Macau, China;University of Texas, Health Science Center at San Antonio;University of Texas at San Antonio and University of Texas, Health Science Center at San Antonio
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

Citing 2
Cited 0

Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Prediction of both conserved and nonconserved microRNA targets in animals

Bioinformatics

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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.