P. Xuan, M.Z. Guo, J. Wang, C.Y. Wang, X.Y. Liu, Y. Liu
Published: April 12, 2011
Genet. Mol. Res. 10(2): 588-603
DOI: https://doi.org/10.4238/vol10-2gmr969

Cite this Article:
P. Xuan, M.Z. Guo, J. Wang, C.Y. Wang, X.Y. Liu, Y. Liu (2011). Genetic algorithm-based efficient feature selection for classification of pre-miRNAs. Genet. Mol. Res. 10(2): 588-603. https://doi.org/10.4238/vol10-2gmr969

About the Authors
P. Xuan, M.Z. Guo, J. Wang, C.Y. Wang, X.Y. Liu, Y. Liu
Corresponding Author: M.Z. Guo
Email: maozuguo@hit.edu.cn

ABSTRACT

In order to classify the real/pseudo human precursor microRNA (pre-miRNAs) hairpins with ab initio methods, numerous features are extracted from the primary sequence and second structure of pre-miRNAs. However, they include some redundant and useless features. It is essential to select the most representative feature subset; this contributes to improving the classification accuracy. We propose a novel feature selection method based on a genetic algorithm, according to the characteristics of human pre-miRNAs. The information gain of a feature, the feature conservation relative to stem parts of pre-miRNA, and the redundancy among features are all considered. Feature conservation was introduced for the first time. Experimental results were validated by cross-validation using datasets composed of human real/pseudo pre-miRNAs. Compared with microPred, our classifier miPredGA, achieved more reliable sensitivity and specificity. The accuracy was improved nearly 12%. The feature selection algorithm is useful for constructing more efficient classifiers for identification of real human pre-miRNAs from pseudo hairpins.

Key words: Feature selection, Genetic algorithm, Pre-miRNA, Information gain, Conservation.