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

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.

关键词： siRNA Quantitative prediction Random forest regression

作者: Peng Jiang Xiao Sun Zuhong Lu

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

会议类型: 国际会议

会议名称: The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007)(首届IEEE生物信息与生物医学工程国际会议)

会议地点: 武汉

会议语种:英文

页码: 234-237

在线出版日期: 2007-07-06（万方平台首次上网日期，不代表论文的发表时间）