Document Detail


Genetic algorithm-based efficient feature selection for classification of pre-miRNAs.
MedLine Citation:
PMID:  21491369     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
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.
Authors:
P Xuan; M Z Guo; J Wang; C Y Wang; X Y Liu; Y Liu
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Publication Detail:
Type:  Journal Article     Date:  2011-04-12
Journal Detail:
Title:  Genetics and molecular research : GMR     Volume:  10     ISSN:  1676-5680     ISO Abbreviation:  Genet. Mol. Res.     Publication Date:  2011  
Date Detail:
Created Date:  2011-04-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101169387     Medline TA:  Genet Mol Res     Country:  Brazil    
Other Details:
Languages:  eng     Pagination:  588-603     Citation Subset:  IM    
Affiliation:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China.
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