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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A tripartite clustering analysis on microRNA, gene and disease model
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A tripartite clustering analysis on microRNA, gene and disease model

机译：microRNA，基因和疾病模型的三方聚类分析

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摘要

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.
机译：响应调节分子或突变的基因表达改变可能导致不同的疾病。已经发现MicroRNA（miRNA）参与基因表达的调节和多种疾病。在人类miRNA的三重生物网络中，它们的预测靶基因以及这些基因表达改变引起的疾病，关于miRNA致病性的有价值知识，涉及的基因和相关疾病类别可以通过共同聚类miRNA，靶基因和疾病同时发生。与仅使用两种成员的传统共聚相比，三方共聚可带来更多信息，并且通过考虑多类型成员可将隐藏的关系信息沿关系链传递。在这里，我们报告了一种用于k部分图的频谱共聚算法，以查找具有异构成员的聚类。我们使用该方法来探索miRNA，基因和疾病之间的潜在关系。从算法中获得的聚类比随机选择的聚类具有更高的密度，这意味着同一聚类中的成员更有可能具有公共连接。结果还显示，基于发夹序列的同一家族中的miRNA倾向于属于同一簇。我们还通过检查同一聚类中丰富的基因功能与疾病类别的相关性来验证聚类结果。最后，作为案例研究，分析了广泛研究的miR-17-92及其旁系同源物，揭示了与miRNA共同簇集的基因和疾病符合当前的研究结果。

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