Document Detail

Genetic algorithm-based efficient feature selection for classification of pre-miRNAs.
MedLine Citation:
PMID:  21491369     Owner:  NLM     Status:  In-Data-Review    
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.
P Xuan; M Z Guo; J Wang; C Y Wang; X Y Liu; Y Liu
Related Documents :
23755139 - Mortality attributable to seasonal and pandemic influenza, australia, 2003 to 2009, usi...
12944119 - Relationship between detection limit and bias of accuracy of quantification of rna by r...
17073349 - Statistical distribution of the measure of coherence.
24950499 - The association of lur modeled pm2.5 elemental composition with personal exposure.
24052559 - A unifying conceptual model for the environmental responses of isoprene emissions from ...
23695869 - Lack of impact on polyp detection by fellow involvement during colonoscopy: a meta-anal...
9034859 - Memory for places: a navigational model in support of marr's theory of hippocampal func...
22493539 - Mapt and paice: tools for time series and single time point transcriptionist visualizat...
23866989 - Inferential explanations in biology.
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    
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Molecular characterization and structure analysis of RPL10/QM-like protein from the red drum Sciaeno...
Next Document:  ?-casein gene expression by in vitro cultured bovine mammary epithelial cells derived from developin...