School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia.
BMC Bioinformatics. 2009 Dec 10;10:408. doi: 10.1186/1471-2105-10-408.
microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs.
We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future.
This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.
microRNAs（miRNAs）通过控制其 mRNA 的转录后水平来调节靶基因的表达。越来越多的证据表明，miRNAs 在各种生物过程中发挥着重要作用。然而，大多数 miRNAs 的功能和精确调控机制仍不清楚。目前的研究表明，miRNA 调控模块很复杂，包括不同生理条件下的上调、下调和混合调控。以前用于发现 miRNA-mRNA 相互作用的计算方法仅关注下调调控模块。在这项工作中，我们提出了一种方法来捕获包括 miRNA 和 mRNA 之间所有调控类型的复杂 miRNA-mRNA 相互作用。
我们提出了一种使用贝叶斯网络结构学习与分裂平均策略来捕获复杂 miRNA-mRNA 相互作用的方法。它旨在通过整合 miRNA 靶向信息、miRNAs 和 mRNAs 的表达谱以及样本类别来探索所有可能的 miRNA-mRNA 相互作用。我们还对上皮间质转化（EMT）数据集进行了分析。我们的结果表明，所提出的方法从数据中识别出了所有可能类型的 miRNA-mRNA 相互作用。许多相互作用具有巨大的生物学意义。一些发现已经被以前的研究验证，例如，miR-200 家族通过 EMT 负调控 ZEB1 和 ZEB2。有些与文献一致，例如 LOX 与 miR-200 家族成员有广泛的相互作用用于 EMT。此外，许多新的相互作用在统计学上是显著的，并且值得在不久的将来进行验证。
本文提出了一种新的方法，使用贝叶斯网络结构学习与分裂平均策略来探索不同生理条件下的复杂 miRNA-mRNA 相互作用。该方法利用包括 miRNA 靶向信息、miRNAs 和 mRNAs 的表达谱以及样本类别在内的异构数据。EMT 数据集上的结果表明，所提出的方法揭示了许多已知的 miRNA 靶标以及新的潜在有前途的 miRNA-mRNA 相互作用。这些相互作用是正常的贝叶斯网络结构学习无法实现的。
