Title :
MiRPara: A SVM-based software for prediction of mature miRNAs
Author :
Wu, Yonggan ; Wei, Bo ; Liu, Haizhou ; Han, Na ; Rayner, Simon
Author_Institution :
State Key Lab. of Virology, Chinese Acad. of Sci., Wuhan, China
Abstract :
miRPara, is a new software tool that predicts mature miRNA in a species specific manner based on three SVM trained models from a comprehensive set of different parameters related to the physical properties of the pre-miRNA and its miRNAs. It achieves an accuracy of up to 80% for animal and virus sequences against experimentally verified mature miRNAs predicted from long genome sequences, making it one of the most accurate methods available. Because of the greater diversity of the plant miRNAs, only 70% accuracy was achieved, but this was nevertheless more accurate than results obtained with other prediction software.
Keywords :
bioinformatics; genomics; macromolecules; molecular biophysics; organic compounds; software tools; support vector machines; SVM trained model; SVM-based software; animal sequences; long genome sequences; mature miRNA prediction; miRPara; software tool; virus sequences; SVM; component; formatting; miRNA; predict;
Conference_Titel :
Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on
Conference_Location :
Hong, Kong
Print_ISBN :
978-1-4244-8303-7
Electronic_ISBN :
978-1-4244-8304-4
DOI :
10.1109/BIBMW.2010.5703935
