Title :
A comprehensive study of a SVM-based miRNA target prediction algorithm
Author :
Liu, Hui ; Yue, Dong ; Chen, Yidong ; Yufei Huang
Author_Institution :
SIEE, China Univ. of Min. & Technol., Xuzhou, China
Abstract :
MicroRNAs are single-stranded non-coding RNAs that play important regulatory roles in many biological processes and diseases. Identifying miRNA regulatory targets is paramount in elucidating its function. We carried out a comprehensive study of a new SVM-based target prediction algorithm called SVMicrO in this paper. The training data set is carefully derived from the most up-to-date collection of verified targets and multiple microarray data sets. Several varieties of feature design and selection schemes are investigated. The prediction results are compared with most of the existing algorithms, which show improved sensitivity and specificity of this two-stage SVM algorithm.
Keywords :
biology computing; macromolecules; molecular biophysics; support vector machines; SVM-based miRNA target prediction algorithm; SVMicrO; biological process; disease; miRNA regulatory target identification; microRNA; multiple microarray data set; single-stranded noncoding RNA; support vector machines; training data set; two-stage SVM algorithm; Bioinformatics; Cancer; Feature extraction; Genomics; Pediatrics; Prediction algorithms; RNA; Spatial databases; Support vector machines; Testing;
Conference_Titel :
Genomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop on
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-4761-9
Electronic_ISBN :
978-1-4244-4762-6
DOI :
10.1109/GENSIPS.2009.5174346
