An effective method for network module extraction from microarray data

BMC Bioinformatics

Research

Priyakshi Mahanta¹  Hasin A Ahmed¹  Dhruba K Bhattacharyya¹  Jugal K Kalita² 

[1] Dept. of Comp. Sc. and Engg, Tezpur University, Napaam, Tezpur, India;Dept. of Computer Science, University of Colorado, Colorado, Springs, USA;

关键词: Span Tree;    Network Module;    Connected Region;    Soft Thresholding;    Hard Thresholding;   

DOI  :  10.1186/1471-2105-13-S13-S4

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsThis 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.ConclusionsOur 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.

【 授权许可】

Unknown   
© Mahanta et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.