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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/8563

Title: Prediction of human microRNA hairpin using only positive sample learning
Authors: Tran, Dang Hung
Tho, Hoam Pham
Satou, Kenji
Ho, Tu Bao
Keywords: MicroRNA
Hairpin
One-class SVM
Issue Date: 2008-08
Publisher: Scientific Research Publishing
Magazine name: Journal of Biomedical Science and Engineering
Volume: 1
Number: 2
Start page: 141
End page: 146
DOI: 10.4236/jbise.2008.12023
Abstract: MicroRNAs(miRNA) are small molecular non-coding RNAs that have important roles in the post-transcriptional mechanism of animal and plant. They are commonly 21-25 nucleotides (nt) long and derived from 60-90 nt RNA hairpin structures, called miRNA hairpins. A larger number of sequence segments in the human genome have been computationally identified with such 60-90 nt hairpins, however a majority of them are not miRNA hairpins. Most existing computational methods for predicting miRNA hairpins are based on a two-class classifier to distinguish between miRNA hairpins and other sequence segments with hairpin structures. The difficulty of these methods is how to select hairpins as negative examples of miRNA hairpins in the training datasets, since only a few miRNA hairpins are available. Therefore, these classifiers may be mis-trained due to some false negative examples of the training dataset. In this paper, we introduce a one-class support vector machine (SVM) method to predict miRNA hairpins among the hairpin structures. Different from existing methods for predicting miRNA hairpins, the one-class SVM classifier is trained only on the information of the miRNA class. We also illustrate some examples of predicting miRNA hairpins in human chromosomes 10, 15, and 21, where our method overcomes the above disadvantages of existing two-class methods.
Rights: Copyright (C) 2008 Scientific Research Publishing. Dang Hung Tran, Tho Hoan Pham, Kenji Satou, Tu Bao Ho, Journal of Biomedical Science and Engineering, 1(2), 2008, 141-146. http://dx.doi.org/10.4236/jbise.2008.12023
URI: http://hdl.handle.net/10119/8563
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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