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Network analysis

  • Alessandro Cellerino
  • Michele Sanguanini
Part of the CRM Series book series (PSNS, volume 17)

Abstract

Life is built on functional interactions—between molecules, macro-molecular complexes, subcellular organells, cells and any other higherlevel organisation. If we consider a set of genes and their expression changes across biological conditions, we could be interested to test whether these coordinated changes might suggest functional interactions among subsets of genes. The clustering methods we described in Chapter 5 are the standard methods to reveal structures within gene co-expression patterns. Since the early 2000s, graph theory has been increasingly applied to biological datasets in order to build genome-scale networks such as
  • protein-protein interaction network, also called interactome,

  • knowledge-based networks, such as KEGG pathways, which are built upon findings from the scientific literature, or

  • gene co-expression networks.

In this chapter, we will focus on the applications of graph theory to the study of gene co-expression networks.

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Copyright information

© Scuola Normale Superiore Pisa 2018

Authors and Affiliations

  • Alessandro Cellerino
    • 1
  • Michele Sanguanini
    • 2
  1. 1.Scuola Normale SuperiorePisaItaly
  2. 2.Gonville and Caius CollegeUniversity of CambridgeCambridge, CambridgeshireUnited Kingdom

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