期刊
GENETICS AND MOLECULAR RESEARCH
卷 10, 期 2, 页码 588-603出版社
FUNPEC-EDITORA
DOI: 10.4238/vol10-2gmr969
关键词
Feature selection; Genetic algorithm; Pre-miRNA; Information gain; Conservation
资金
- Chinese Natural Science Foundation [60932008, 60871092]
- Fundamental Research Funds for the Central Universities [HIT.ICRST.2010 022]
- China Postdoctoral Science Special Foundation [201003446]
- Returned Scholar Foundation of Educational Department of Heilongjiang Province in China [1154hz26]
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.
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