A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information.
A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information.
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作者：
Wang Z;Xu W;Zhu H;Liu Y
microRNA（miRNAs）是一类小分子调控RNA，在多种生物学过程中发挥重要的基因调控作用，特别是在癌症的发生发展中。因此，推断miRNA靶点是充分理解miRNA在调节肿瘤发生中的功能特性的必要步骤。贝叶斯线性回归模型已经被提出用于基于整合的序列信息和匹配的miRNA和mRNA表达数据来识别miRNA和mRNA之间的相互作用;然而，这种方法没有使用推定的miRNA靶的可用特征的全谱。在这项研究中，我们整合了miRNA靶向的四个重要序列和结构特征与配对的miRNA和mRNA表达数据，以改善贝叶斯框架中的miRNA靶向预测。我们已经将这种方法应用于肝癌患者的基因表达研究，并检查了每个miRNA-mRNA相互作用在肝癌发展中起作用的后验概率。我们的方法取得了更好的性能，在确定的真实目标的数量方面，比其他方法。
MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA–mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.