Introduces new graph theory techniques for the analysis and integration of multi-type large data sets) in the life sciences. Discussing cutting-edge problems and techniques, this book provides researchers from a wide range of fields with methods for exploiting big heterogeneous data in biology through the concept of 'network of networks'.
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'.
Arvustused
' Networks of Networks in Biology should be of interest and a good introductory resource for molecular biologists, cell biologists, and biochemists, as well as bioinformaticians not yet acquainted with multilayer networks.' Ingo Brigandt, Quarterly Review of Biology
Muu info
Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.
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vii | |
| Preface |
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ix | |
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Part I Networks in Biology |
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1 | (32) |
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1 An Introduction to Biological Networks |
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3 | (15) |
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Xabier Martinez de Morentin |
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18 | (15) |
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33 | (50) |
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3 Structural Analysis of Biological Networks |
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35 | (21) |
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4 Networks from an Information-Theoretic and Algorithmic Complexity Perspective |
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56 | (15) |
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5 Integration and Feature Identification in Multi-layer Networks using a Heat Diffusion Approach |
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71 | (12) |
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Part III Multi-layer Networks |
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83 | (62) |
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85 | (20) |
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7 Existing Tools for Analysis of Multi-layer Networks |
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105 | (16) |
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8 Dynamics on Multi-layer Networks |
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121 | (24) |
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145 | (50) |
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9 The Network of Networks Involved in Human Disease |
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147 | (25) |
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10 Towards a Multi-layer Network Analysis of Disease: Challenges and Opportunities Through the Lens of Multiple Sclerosis |
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172 | (11) |
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11 Microbiome: A Multi-layer Network View is Required |
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183 | (12) |
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195 | (6) |
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12 Concluding Remarks: Open Questions and Challenges |
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197 | (4) |
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| Index |
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201 | |
Narsis A. Kiani is Assistant Professor and the Co-leader of Algorithmic Dynamics in the Department of Oncology-Pathology of the Karolinska Institutet, Sweden. She is passionate about mathematics and is interested in the fundamental question of what observations about the effects at the microscopic level can tell us about the macroscopic nature of biological systems and vice versa, and how defects and disorder affect these systems. David Gomez-Cabrero is the Head of the Translational Bioinformatics Unit at Navarrabiomed, Spain. Since 2009, he has specialised in bioinformatics and data integration analysis, first as a post-doctorate and subsequently as Assistant Professor at the Karolinska Institutet, Sweden, and as Senior Lecturer at King's College London, UK. He collaborates with clinical groups that investigate multiple sclerosis, rheumatoid arthritis, chronic obstructive pulmonary disease (COPD) and cancer, among other diseases. Ginestra Bianconi is Professor of Applied Mathematics at Queen Mary University of London and a Turing Fellow at the Alan Turing Institute. She is Editor-in-Chief of the Journal of Physics: Complexity and Editor of Scientific Reports, PloS One. She has published more than 150 articles in network theory and interdisciplinary applications. She has authored the book Multilayer Networks: Structure and Function (2018).
