Department of Computer Science, and Engg, Tezpur University, Napaam, Tezpur, India.
BMC Bioinformatics. 2012;13 Suppl 13(Suppl 13):S4. doi: 10.1186/1471-2105-13-S13-S4. Epub 2012 Aug 24.
The development of high-throughput Microarray technologies has provided various opportunities to systematically characterize diverse types of computational biological networks. Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules.
This paper presents a method to build a co-expression network (CEN) and to detect network modules from the built network. We use an effective gene expression similarity measure called NMRS (Normalized mean residue similarity) to construct the CEN. We have tested our method on five publicly available benchmark microarray datasets. The network modules extracted by our algorithm have been biologically validated in terms of Q value and p value.
Our results show that the technique is capable of detecting biologically significant network modules from the co-expression network. Biologist can use this technique to find groups of genes with similar functionality based on their expression information.
高通量微阵列技术的发展为系统地描述各种类型的计算生物网络提供了多种机会。共表达网络在分析微阵列数据方面变得越来越流行，例如用于检测功能基因模块。
本文提出了一种构建共表达网络（CEN）和从构建的网络中检测网络模块的方法。我们使用一种称为 NMRS（归一化平均残基相似性）的有效基因表达相似性度量来构建 CEN。我们已经在五个公开可用的基准微阵列数据集上测试了我们的方法。我们的算法提取的网络模块在 Q 值和 p 值方面已经过生物学验证。
我们的结果表明，该技术能够从共表达网络中检测出具有生物学意义的网络模块。生物学家可以使用该技术根据基因表达信息找到具有相似功能的基因群。
