基于基因表达数据集的共表达网络局部模块化结构预测。

Yoshiyuki Ogata, Nozomu Sakurai, Hideyuki Suzuki, Koh Aoki, Kazuki Saito, Daisuke Shibata
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引用次数: 0

摘要

在系统生物学等科学领域,网络成员(顶点)之间的关系的评估是使用网络结构来进行的。在共表达网络中,由基因(顶点)和代表共表达关系的基因-基因链接(边)组成,具有紧密模块内连接的局部模块结构包括彼此共表达的基因。为了从整个网络中检测这些模块,一种评估模块间网络拓扑和模块内网络拓扑的方法是有用的。为了检测这些模块,我们将一种新颖的模块间指数与网络密度相结合,即具有代表性的模块内指数,而不是单一使用网络密度。我们设计了一种优化模块组合索引的算法,并将其应用于拟南芥共表达分析。为了验证使用我们的算法获得的模块与生物学知识之间的关系,我们将其与使用KEGG途径进行共表达网络分析的其他工具进行了比较,表明我们的算法检测到的网络模块与这些途径有更好的关联。它也适用于大量难以计算的基因表达谱数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The prediction of local modular structures in a co-expression network based on gene expression datasets.

In scientific fields such as systems biology, evaluation of the relationship between network members (vertices) is approached using a network structure. In a co-expression network, comprising genes (vertices) and gene-to-gene links (edges) representing co-expression relationships, local modular structures with tight intra-modular connections include genes that are co-expressed with each other. For detecting such modules from among the whole network, an approach to evaluate network topology between modules as well as intra-modular network topology is useful. To detect such modules, we combined a novel inter-modular index with network density, the representative intra-modular index, instead of a single use of network density. We designed an algorithm to optimize the combinatory index for a module and applied it to Arabidopsis co-expression analysis. To verify the relation between modules obtained using our algorithm and biological knowledge, we compared it to the other tools for co-expression network analyses using the KEGG pathways, indicating that our algorithm detected network modules representing better associations with the pathways. It is also applicable to a large dataset of gene expression profiles, which is difficult to calculate in a mass.

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