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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks
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Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks

机译：在双层异构网络上加权PageS优先考虑2型糖尿病基因

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摘要

The prevalence of diabetes mellitus has been increasing rapidly in recent years. Type 2 diabetes makes up about 90 percent cases of diabetes. The interacting mixed effects of genetics and environments build possible interpretable pathogenesis. Thus, finding the causal disease genes is crucial in its clinical diagnosis and medical treatment. Currently, network-based computational method becomes a powerful tool of systematically analyzing complex diseases, such as the identification of candidate disease genes from networks. In this paper, we propose a bioinformatics framework of prioritizing type 2 diabetes genes by leveraging the modified PageRank algorithm on bilayer biomolecular networks consisting an ensemble gene-gene regulatory network and an integrative protein-protein interaction network. We specifically weigh the networks by differential mutual information for measuring the context specificities between genes and between proteins by transcriptomic and proteomic datasets, respectively. After formulating the network into two components of known disease genes and the other normal healthy genes, we rank the diabetes genes and others by bringing the orders in the bilayer network via an improved PageRank algorithm. We conclude that these known disease genes achieve significantly higher ranks compared to these randomly-selected normal genes, and the ranks are robust and consistent in multiple validation scenarios. In functional analysis, these high-ranked genes are identified to perform relevant risks and dysfunctions of type 2 diabetes.
机译：近年来，糖尿病的患病率迅速增加。 2型糖尿病占糖尿病患者约90％。遗传和环境的相互作用的混合效应构建可能的可解释性发病机制。因此，寻找因果疾病基因在其临床诊断和医疗中至关重要。目前，基于网络的计算方法成为系统地分析复杂疾病的强大工具，例如来自网络的候选疾病基因的鉴定。在本文中，我们提出了通过利用组成的Bilayer生物分子网络的改性PageRank算法以及组成的细胞基因调节网络和一体化蛋白质 - 蛋白质相互作用网络来提出优先考虑2型糖尿病基因的生物信息框架。我们通过差分相互信息特别称重网络，用于测量基因之间的上下文特异性和通过转录组和蛋白质组学数据集之间的蛋白质之间的特异性。在将网络配制成已知疾病基因和其他正常健康基因的两种组分后，通过通过改进的PageRank算法将订单中的订单带入双层网络中，从而排序糖尿病基因等。我们得出结论，与这些随机选择的正常基因相比，这些已知的疾病基因达到了显着更高的等级，并且在多种验证方案中，级别是稳健的并且一致。在功能分析中，鉴定了这些高级基因以表现2型糖尿病的相关风险和功能障碍。

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