DocumentCode :
3047974
Title :
Identification of 5´ Pre-miRNAs and 3´ Pre-miRNAs Employing Support Vector Machine and Local Structure Units
Author :
Weibo Jin ; Dong Kong ; Wu, Fangli ; Guo, Aiguang
Author_Institution :
Coll. of Life Sci., Northwest A&F Univ., Yangling
fYear :
2007
fDate :
6-8 July 2007
Firstpage :
268
Lastpage :
271
Abstract :
MicroKNAs (miRNA) are essential 21-22 nucleotides regulatory RNAs produced from larger hairpin precursors (pre-miRNA), and regulate gene expression through mRNA degradation or translational inhibition. We applied functional strand support vector machine (FS-SVM), a new method for prediction of functional strand on the miRNA precursors, to classifying 5´ and 3´ pre-miRNAs and achieved about 94% accuracy on human or mouse data. The FS-SVM classifier built on human and mouse data can correctly identify up to 90.9% of the pre-miRNAs from primates, and up to about 89.0% of the pre-miRNAs from other mammals.
Keywords :
biology computing; cellular biophysics; molecular biophysics; support vector machines; FS-SVM classifier; functional strand support vector machine; gene expression; hairpin precursors; human data; local structure units; mRNA degradation; mammals; mouse data; nucleotides; pre-miRNAs; translational inhibition; Agriculture; Biology; Degradation; Gold; Hidden Markov models; Humans; Mice; RNA; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on
Conference_Location :
Wuhan
Print_ISBN :
1-4244-1120-3
Type :
conf
DOI :
10.1109/ICBBE.2007.72
Filename :
4272556
Link To Document :
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