Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.
Author(s): Narsis A. Kiani, David Gomez-Cabrero, Ginestra Bianconi
Publisher: Cambridge University Press
Year: 2021
Language: English
Pages: 214
City: Cambridge
Frontmatter
Contents
List of Contributors
Preface
Part I Networks in Biology
1 An Introduction to Biological Networks
2 Graph Theory
Part II Network Analysis
3 Structural Analysis of Biological Networks
4 Networks from an Information-Theoretic and Algorithmic Complexity Perspective
5 Integration and Feature Identiļ¬cation in Multi-layer Networks using a Heat Diffusion Approach
Part III Multi-layer Networks
6 Multiplex Networks
7 Existing Tools for Analysis of Multi-layer Networks
8 Dynamics on Multi-layer Networks
Part IV Applications
9 The Network of Networks Involved in Human Disease
10 Towards a Multi-layer Network Analysis of Disease
11 Microbiome: A Multi-layer Network View is Required
Part V Conclusion
12 Concluding Remarks: Open Questions and Challenges
Index
