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Applications of Network Analysis in Biomedicine

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Precision Medicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2204))

Abstract

The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug–target identification, and multi-level omics integration.

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Authors and Affiliations

  1. Department of Biological Sciences, Columbia University, New York, NY, USA

    Steven Wang

  2. Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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  1. Steven Wang
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  2. Tao Huang
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Editors and Affiliations

  1. Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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Wang, S., Huang, T. (2020). Applications of Network Analysis in Biomedicine. In: Huang, T. (eds) Precision Medicine. Methods in Molecular Biology, vol 2204. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0904-0_4

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  • DOI: https://doi.org/10.1007/978-1-0716-0904-0_4

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0903-3

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