@inbook{b9b0c249a1664da3a1438aed007209f7,
title = "Network Analysis of Microarray Data",
abstract = "DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification. In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.",
keywords = "Coexpression, Differential coexpression, Microarray, Multilayer networks, Pathways",
author = "Alisa Pavel and Angela Serra and Luca Cattelani and Antonio Federico and Dario Greco",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2022",
doi = "10.1007/978-1-0716-1839-4\_11",
language = "English",
isbn = "978-1-0716-1841-7",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "161--186",
editor = "Giuseppe Agapito",
booktitle = "Microarray Data Analysis",
address = "United States",
}