Discovery of microRNA-mRNA modules via population-based probabilistic learning
Discovery of microRNA-mRNA modules via population-based probabilistic learning
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DOI：
10.1093/bioinformatics/btm045
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发表时间：
2007-05-01
期刊：
影响因子：
5.8
通讯作者：
Zhang, Byoung-Tak
中科院分区：
文献类型：
--
作者：
Joung, Je-Gun;Hwang, Kyu-Baek;Zhang, Byoung-Tak
动机：microRNAs(MiRNAs)和mRNAs构成了基因调控网络的重要组成部分，影响着各种生物现象。阐明密切相关的miRNAs和mRNAs可能是发现它们对不同细胞状态的组合作用的必要的第一步。在这里，我们提出了一种概率学习方法来从多个信息源中识别涉及其条件特定靶基因(MRNAs)调控的协同miRNAs，即计算预测的miRNAs的靶基因及其各自的表达谱。结果：我们使用了由miRNA靶基因结合信息以及miRNAs和mRNAs在人类癌症样本上的表达谱组成的数据集。我们的方法允许我们通过使用平衡的适应度函数和对多个种群的有效搜索来从多个数据源检测涉及特定生物过程的功能相关的miRNA-mRNA模块。该算法发现了两个miRNA-mRNA模块，它们的表达和生物学功能高度相关。此外，当相关的miRNAs高度表达时，同一模块中包含的mRNAs显示出更高的相关性，这表明我们的方法能够找到连贯的miRNA-mRNA模块。据报道，这些模块中的大多数成员与癌症密切相关。因此，我们的方法可以提供miRNA的主要来源和被认为构成基因调控途径密切相关部分的靶集。
Motivation: MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e. computationally predicted target genes of miRNAs and their respective expression profiles.Results: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs; included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.