Quantitative Estimation of siRNAs Gene Silencing Capability by Random Forest Regression Model

扫码查看

原文链接

NETL

外文摘要：Although the observations concerning the factors which influence the siRNA efficacy give clues to the mechanism of RNAi， the quantitative prediction of the siRNA efficacy is still a challenge task。 In this paper， we introduced a novel non-linear regression method: random forest regression (RFR)， to quantitatively estimate siRNAs efficacy values。 Compared with an alternative machine learning regression algorithm， support vector machine regression (SVR) and four other score-based algorithms (Reynolds et al。 (2004)， Ui-Tei et al。 (2004)， Hsieh et al。 (2004)， Amarzguioui et al。 (2004)) our RFR model achieved the best performance of all。

外文关键词：

siRNAQuantitative predictionRandom forest regression

作者：

Peng Jiang、Xiao Sun、Zuhong Lu

展开 >

作者单位：

State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China

State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southea

会议名称：

The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007)

会议地点：

Wuhan(CN)

会议母体文献：

International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007); 20070706-08; Wuhan(CN)

页码：

P.234-237

出版时间：

2007