Abstract
Modeling and analyses of complex systems using network theory have been an object of study for a long time. They have caught attention in many disciplines such as sociology, epidemiology, ecology, psychology, biology, biomedicine, and other fields. Network theory is especially an efficient tool to model biological networks such as gene co-expression networks, protein-protein interaction networks, or pathways. Considering the enhanced resolutions of complex real-world systems, the interest has been directed to multilayered networks. However, despite this surge of recent attention, the use of the multilayer framework in the biological field is still in its youth. In this paper, we review the different aspects and terminologies of multilayered networks. We also briefly discuss the variant applications of the multilayer framework, and finally, we give an overview of various existing applications of the multilayer model in network biology.
Highlights
Network theory has been used for many years in the modeling and analysis of complex systems
In this review, we address the lack of a terminology convention in the field of multilayered networks, by distinguishing their different abstract formulation and aspects
Implementations of multilayered networks we present to you the main types and implementations of the multilayer framework
Summary
We address the lack of a terminology convention in the field of multilayered networks, by distinguishing their different abstract formulation and aspects. We distinguish the appropriate applications, based on its structure and the state-of-the-art articles. We describe and illustrate the different implementations of these aspects, with their assets and usage in previous research. We enumerate the various published state-of-the-art articles, in which the authors used a multilayer framework to model networks in Biomedicine, and solve different biomedical problems. We discuss the convenience of this usage and the reasons to choose specific implementations to solve specific problems
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