Gene expression analysis through network biology: Bioinformatics approaches
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Publication Details
Author list: Kusonmano K.
Publisher: Springer Verlag Kg
Publication year: 2017
Journal: Advances in Biochemical Engineering Biotechnology (0724-6145)
Volume number: 160
Start page: 15
End page: 32
Number of pages: 18
ISBN: 978-3-319-56459-3
ISSN: 0724-6145
Languages: English-Great Britain (EN-GB)
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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. ฉ 2016, Springer International Publishing Switzerland.
Keywords
Biological network analysis, Interactome
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