An effective method for network module extraction from microarray data

• Published in: BMC bioinformatics Vol. 13 Suppl 13; no. Suppl 13; p. S4
• Main Authors: Mahanta, Priyakshi, Ahmed, Hasin A, Bhattacharyya, Dhruba K, Kalita, Jugal K
• Format: Journal Article
• Language: English
• Published: England BioMed Central 2012; BioMed Central Ltd; BMC
• Subjects: Algorithms; Bioinformatics; Computational Biology - methods; Computer applications; Computer programs; Data Interpretation, Statistical; Data processing; Databases, Genetic - statistics & numerical data; Gene Expression; Gene Expression Profiling - statistics & numerical data; Gene Regulatory Networks; Genomics; N.M.R; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Studies
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-13-S13-S4

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.