Comparison and Integration of Target Prediction Algorithms for microRNA Studies

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doi doi:10.2390/biecoll-jib-2010-127
submission February 20, 2010
published March 25, 2010
NCBI PubMed PubMed ID 20375447

Yanju Zhang and Fons J Verbeek

Correspondence should be addressed to:
Yanju Zhang
LIACS, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
ln.scail@nullujnay


Abstract

microRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions. The prediction accuracy remains uncertain. In this paper, three commonly used target prediction algorithms are evaluated and further integrated using algorithm combination, ranking aggregation and Bayesian Network classification. Our results revealed that each individual prediction algorithm displays its advantages as was shown on different test data sets. Among different integration strategies, the application of Bayesian Network classifier on the features calculated from multiple prediction methods significantly improved target prediction accuracy.

Reference

Yanju Zhang and Fons J Verbeek. Comparison and Integration of Target Prediction Algorithms for microRNA Studies. Journal of Integrative Bioinformatics, 7(3):127, 2010. Online Journal: http://journal.imbio.de/index.php?paper_id=127
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