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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Multi-Block Bipartite Graph for Integrative Genomic Analysis
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Multi-Block Bipartite Graph for Integrative Genomic Analysis

机译：用于集成基因组分析的多块二部图

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摘要

Human diseases involve a sequence of complex interactions between multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA Methylation (DM), and their interactions simultaneously play an important role in human diseases. However, despite the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intraand inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate multiple genomic data but also incorporate intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g., gene expression, survival time) from the multi-block genomic data. The performance was assessed by simulation experiments that implement practical situations. We also applied the method to the human brain data of psychiatric disorders. The experimental results were analyzed by maximum edge biclique and biclustering, and biological findings were discussed.
机译：人类疾病涉及多个生物过程之间的一系列复杂相互作用。特别是，多个基因组数据，例如单核苷酸多态性（SNP），拷贝数变异（CNV），DNA甲基化（DM）及其相互作用在人类疾病中起着重要作用。然而，尽管众所周知的复杂的多层生物学过程和异构基因组数据的可用性增加，但是大多数研究仅考虑了一种类型的基因组数据。此外，由于高维性和复杂性，尤其是考虑到它们的内部和块间相互作用时，最近针对多个基因组数据的综合基因组学研究也面临困难。在本文中，我们将介绍一种新颖的多块二部图及其推断方法MB2I和sMB2I，以进行综合基因组研究。所提出的方法不仅整合了多个基因组数据，而且还通过使用多区块二分图整合了区块内/区块间的相互作用。另外，该方法可用于从多区块基因组数据预测定量性状（例如，基因表达，存活时间）。通过实施实际情况的模拟实验评估了性能。我们还将该方法应用于精神疾病的人脑数据。通过最大边缘双斜度和双簇分析法对实验结果进行了分析，并对生物学发现进行了讨论。

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