Volume 5 Number 3 (May 2015)
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IJBBB 2015 Vol.5(3): 149-156 ISSN: 2010-3638
doi: 10.17706/ijbbb.2015.5.3.149-156

Incorporating Genome-Wide Co-expression Information to Improve the Analysis of Gene Expression Data

Yinglei Lai
Abstract—Although microarray and deep sequencing technologies allow us to monitor gene expression at a genomic level, they also generate a relatively high false discovery rate, which limits our further exploration of disease mechanism. Furthermore, it is usually difficult to detect disease related genes with weak differential expression. It is well known that genes interact with each other during cellular and molecular processes. Therefore, disease related genes are not isolated. With large scale gene expression data, it is possible to evaluate the genome-wide co-expression information. This information is valuable to understand gene interactions. We have shown that differential expression analysis can be improved when this information can be incorporated. In this study, we demonstrate that genome-wide co-expression information can also clearly improve gene set enrichment analysis and classification analysis.

Index Terms—Expression, co-expression, differential expression, classification.

Department of Statistics, The George Washignton University, Washington, D.C., 20052, U.S.A.

Cite: Yinglei Lai, "Incorporating Genome-Wide Co-expression Information to Improve the Analysis of Gene Expression Data," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 5, no. 3, pp. 149-156, 2015.

General Information

ISSN: 2010-3638 (Online)
Abbreviated Title: Int. J. Biosci. Biochem. Bioinform.
Frequency: Quarterly 
DOI: 10.17706/IJBBB
Editor-in-Chief: Prof. Ebtisam Heikal 
Abstracting/ Indexing:  Electronic Journals Library, Chemical Abstracts Services (CAS), Engineering & Technology Digital Library, Google Scholar, and ProQuest.
E-mail: ijbbb@iap.org
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