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A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information

机译：通过整合外部信息来改善MicroRNA目标预测的贝叶斯框架

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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.
机译：MicroRNA（miRNA）是小的调节性RNA，在各种生物过程中，尤其是在癌症发展中，起着关键的基因调节作用。因此，推断miRNA靶标是充分理解miRNA在调节肿瘤发生中作用的功能特性的重要步骤。已经提出了贝叶斯线性回归模型，以基于整合的序列信息以及匹配的miRNA和mRNA表达数据来鉴定miRNA和mRNA之间的相互作用。但是，这种方法并未使用推定的miRNA靶标的全部可用特征。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以及成对的miRNA和mRNA表达数据，以改善贝叶斯框架中的miRNA靶标预测。我们已经将该方法应用于肝癌患者的基因表达研究，并检查了每种miRNA-mRNA相互作用在肝癌发展中起作用的后验概率。就确定的真实目标数量而言，我们的方法比其他方法具有更好的性能。

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