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Gene Expression Analysis Through Network Biology: Bioinformatics Approaches

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Network Biology

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 160))

Abstract

Following the availability of high-throughput technologies, vast amounts of biological data have been generated. Gene expression is one example of the popular data that has been utilized for studying cellular systems in the transcriptional level. Several bioinformatics approaches have been developed to analyze such data. A typical expression analysis identifies a ranked list of individual significant differentially expressed genes between two conditions of interest. However, it has been accepted that biomolecules in a living organism are working together and interacting with each other. Study through network analysis could be complementary to typical expression analysis and provides more contexts to understanding the biological systems. Conversely, expression data could provide clues to functional links between biomolecules in biological networks. In this chapter, bioinformatics approaches to analyze expression data in network levels including basic concepts of network biology are described. Different concepts to integrate expression data with interactome data and example studies are explained.

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Authors and Affiliations

  1. Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkhuntien, Bangkok, Thailand

    Kanthida Kusonmano

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  1. Kanthida Kusonmano
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Correspondence to Kanthida Kusonmano .

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Editors and Affiliations

  1. Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Science, Little Rock, Arkansas, USA

    Intawat Nookaew

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Kusonmano, K. (2016). Gene Expression Analysis Through Network Biology: Bioinformatics Approaches. In: Nookaew, I. (eds) Network Biology. Advances in Biochemical Engineering/Biotechnology, vol 160. Springer, Cham. https://doi.org/10.1007/10_2016_44

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