miRNAs are key regulators that bind to target genes to suppress their gene expression level. The relations between miRNA-target genes enable users to derive co-expressed genes that may be involved in similar biological processes and functions in cells. We hypothesize that target genes of miRNAs are co-expressed, when they are regulated by multiple miRNAs. With the usage of these co-expressed genes, we can theoretically construct co-expression networks (GCNs) related to 152 diseases. In this study, we introduce ARNetMiT that utilize a hash based association rule algorithm in a novel way to infer the GCNs on miRNA-target genes data. We also present R package of ARNetMiT, which infers and visualizes GCNs of diseases that are selected by users. Our approach assumes miRNAs as transactions and target genes as their items. Support and confidence values are used to prune association rules on miRNA-target genes data to construct support based GCNs (sGCNs) along with support and confidence based GCNs (scGCNs). We use overlap analysis and the topological features for the performance analysis of GCNs. We also infer GCNs with popular GNI algorithms for comparison with the GCNs of ARNetMiT. Overlap analysis results show that ARNetMiT outperforms the compared GNI algorithms. We see that using high confidence values in scGCNs increase the ratio of the overlapped gene-gene interactions between the compared methods. According to the evaluation of the topological features of ARNetMiT based GCNs, the degrees of nodes have power-law distribution. The hub genes discovered by ARNetMiT based GCNs are consistent with the literature.

译文

mirna是与靶基因结合以抑制其基因表达水平的关键调节因子。miRNA-靶基因之间的关系使用户能够获得可能参与细胞中类似生物学过程和功能的共表达基因。我们假设mirna的靶基因被多个mirna调控时是共表达的。通过使用这些共表达基因,我们可以从理论上构建与152疾病相关的共表达网络 (GCNs)。在这项研究中,我们介绍了ARNetMiT,它以一种新颖的方式利用基于哈希的关联规则算法来推断miRNA靶基因数据上的GCNs。我们还介绍了ARNetMiT的R软件包,该软件包推断并可视化了用户选择的疾病的GCNs。我们的方法假设mirna作为交易,目标基因作为它们的项目。支持和置信度值用于修剪miRNA靶基因数据上的关联规则,以构建基于支持的GCNs (sGCNs) 以及基于支持和置信度的GCNs (scGCNs)。我们使用重叠分析和拓扑特征进行GCNs的性能分析。我们还使用流行的GNI算法推断GCNs,以与ARNetMiT的GCNs进行比较。重叠分析结果表明,ARNetMiT优于比较的GNI算法。我们看到,在scGCNs中使用高置信度值会增加比较方法之间重叠的基因-基因相互作用的比率。根据对基于ARNetMiT的GCNs的拓扑特征的评估,节点的度具有幂律分布。基于ARNetMiT的GCNs发现的hub基因与文献一致。

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