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