Disease biomarkers and biological interaction networks
This chapter begins with an introduction to biological networks in the context of health, disease and biomarker discovery, as well as major analysis assumptions and methodological principles. Basic statistical concepts used to analyze the structure of networks and to discover biomedical relevant knowledge are defined. This is followed by an overview of the main approaches to representing and inferring biological networks. An introduction to key network-based approaches to biomarker discovery using different types of ‘omic’ information is presented. The last part of the chapter includes a more detailed discussion of representative examples of methods and applications, and of current limitations and challenges in this area. The next chapter will cover some of the approaches and examples discussed here in greater detail.
7.1 Network-centric views of disease biomarker discovery
The availability of increasing amounts of diverse ‘omic’ datasets together with the need to discover complex, clinically-relevant and more subtle associations between genes and disease have motivated the application of network-based biomarker discovery methodologies. In this approach a network typically consists of a set of nodes and edges, which represent the biological system components and interactions between the components respectively. Examples of network nodes are genes, proteins, drugs and diseases. The edges may encode different types of physical ...