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Cheminformatics Approach to Gene Silencing: Z Descriptors of Nucleotides and SVM Regression Afford Predictive Models for siRNA Potency

机译:基因沉默的化学信息学方法:核苷酸的Z描述子和SVM回归Afford siRNA效能预测模型

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Abstract: Short interfering RNA mediated gene silencing technology has been through tremendous development over the past decade, and has found broad applications in both basic biomedical research and pharmaceutical development. Critical to the effective use of this technology is the development of reliable algorithms to predict the potency and selectivity of siRNAs under study. Existing algorithms are mostly built upon sequence information of siRNAs and then employ statistical pattern recognition or machine learning techniques to derive rules or models. However, sequence-based features have limited ability to characterize siRNAs, especially chemically modified ones. In this study, we proposed a cheminformatics approach to describe siRNAs. Principal component scores (z7, z2, z3, z4) have been derived for each of the 5 nucleotides (A, U, G, C, T) from the descriptor matrix computed by the MOE program.
机译:摘要:短干扰RNA介导的基因沉默技术在过去的十年中经历了巨大的发展,并已在基础生物医学研究和药物开发中得到广泛应用。有效使用该技术的关键是开发可靠的算法来预测所研究siRNA的效能和选择性。现有算法主要基于siRNA的序列信息,然后采用统计模式识别或机器学习技术来推导规则或模型。但是,基于序列的特征表征siRNA的能力有限,尤其是化学修饰的siRNA。在这项研究中,我们提出了一种化学信息学方法来描述siRNA。从由MOE程序计算出的描述符矩阵中,对5个核苷酸(A,U,G,C,T)中的每一个都获得了主成分评分(z7,z2,z3,z4)。

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