DocumentCode
3542741
Title
Computational prediction of microRNA regulatory pathways
Author
Yue, Dong ; Chen, Yidong ; Gao, Shou-Jiang ; Huang, Yufei
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Texas at San Antonio, San Antonio, TX, USA
fYear
2011
fDate
4-6 Dec. 2011
Firstpage
158
Lastpage
161
Abstract
MicroRNAs (miRNAs) are known to regulate transcription and/or protein translation of hundreds of genes. Despite their importance, the functions of most human miRNAs are still poorly understood. In this paper, we proposed a SVM based algorithm - PathMicrO that elucidates the miRNA function by predicting the miRNA regulated pathways. PathMicrO combines the sequence-level target predictions with the gene expression profiling from the miRNA transfection experiments. The performance of PathMicrO is evaluated with cross-validation using a careful constructed training data and two independent testing data. Compared to other prediction algorithms, PathMicrO attains 77% more in sensitivity when false positive rate is equal to 0.21 and achieves much larger area under the receiver operating characteristic (ROC) curve.
Keywords
RNA; biology computing; proteins; support vector machines; PathMicrO; SVM based algorithm; computational prediction; cross-validation; gene expression profiling; gene protein translation; genes transcription regulation; independent testing data; miRNA transfection experiments; microRNA regulatory pathways; receiver operating characteristic curve; sequence-level target predictions; training data; Bioinformatics; Databases; Feature extraction; Gene expression; Histograms; Prediction algorithms; Training data; PathmicrO; SVM; function; microRNA; pathway;
fLanguage
English
Publisher
ieee
Conference_Titel
Genomic Signal Processing and Statistics (GENSIPS), 2011 IEEE International Workshop on
Conference_Location
San Antonio, TX
ISSN
2150-3001
Print_ISBN
978-1-4673-0491-7
Electronic_ISBN
2150-3001
Type
conf
DOI
10.1109/GENSiPS.2011.6169469
Filename
6169469
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