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通过结合文献挖掘和微阵列分析构建生物网络:一种LMMA方法。

Constructing biological networks through combined literature mining and microarray analysis: a LMMA approach.

作者信息

Li Shao, Wu Lijiang, Zhang Zhongqi

机构信息

Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China.

出版信息

Bioinformatics. 2006 Sep 1;22(17):2143-50. doi: 10.1093/bioinformatics/btl363. Epub 2006 Jul 4.

Abstract

MOTIVATION

Network reconstruction of biological entities is very important for understanding biological processes and the organizational principles of biological systems. This work focuses on integrating both the literatures and microarray gene-expression data, and a combined literature mining and microarray analysis (LMMA) approach is developed to construct gene networks of a specific biological system.

RESULTS

In the LMMA approach, a global network is first constructed using the literature-based co-occurrence method. It is then refined using microarray data through a multivariate selection procedure. An application of LMMA to the angiogenesis is presented. Our result shows that the LMMA-based network is more reliable than the co-occurrence-based network in dealing with multiple levels of KEGG gene, KEGG Orthology and pathway.

AVAILABILITY

The LMMA program is available upon request.

摘要

动机

生物实体的网络重建对于理解生物过程和生物系统的组织原则非常重要。这项工作专注于整合文献和微阵列基因表达数据,并开发了一种结合文献挖掘和微阵列分析(LMMA)的方法来构建特定生物系统的基因网络。

结果

在LMMA方法中,首先使用基于文献的共现方法构建一个全局网络。然后通过多变量选择程序使用微阵列数据对其进行优化。展示了LMMA在血管生成中的应用。我们的结果表明,在处理多个层次的KEGG基因、KEGG直系同源基因和通路时,基于LMMA的网络比基于共现的网络更可靠。

可用性

可根据要求提供LMMA程序。

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