Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists

doi:10.1017/9781108377706

The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine.

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Contents

Frontmatter
pp i-iv
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Dedication
pp v-vi
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Contents
pp vii-viii
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List of Contributors
pp ix-xii
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Preface
pp xiii-xiv
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1 - From Genetic Data to Medicine: From DNA Samples to Disease Risk Prediction in Personalized Genetic Tests
pp 1-62
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2 - Epigenetic Data and Disease
pp 63-110
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3 - Introduction to Graph and Network Theory
pp 111-150
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4 - Protein–Protein Interaction Data, their Quality, and Major Public Databases
pp 151-192
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5 - Graphlets in Network Science and Computational Biology
pp 193-240
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6 - Unsupervised Learning: Cluster Analysis
pp 241-285
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7 - Machine Learning for Data Integration in Cancer Precision Medicine: Matrix Factorization Approaches
pp 286-312
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8 - Machine Learning for Biomarker Discovery: Significant Pattern Mining
pp 313-368
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9 - Network Alignment
pp 369-413
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10 - Network Medicine
pp 414-458
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11 - Elucidating Genotype-to-Phenotype Relationships via Analyses of Human Tissue Interactomes
pp 459-489
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12 - Network Neuroscience
pp 490-532
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13 - Cytoscape: A Tool for Analyzing and Visualizing Network Data
pp 533-592
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14 - Analysis of the Signatures of Cancer Stem Cells in Malignant Tumors Using Protein Interactomes and the STRING Database
pp 593-620
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Index
pp 621-632
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Analyzing Network Data in Biology and Medicine

An Interdisciplinary Textbook for Biological, Medical and Computational Scientists

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Contents

Frontmatter
pp i-iv

Dedication
pp v-vi

Contents
pp vii-viii

List of Contributors
pp ix-xii

Preface
pp xiii-xiv

1 - From Genetic Data to Medicine: From DNA Samples to Disease Risk Prediction in Personalized Genetic Tests
pp 1-62

2 - Epigenetic Data and Disease
pp 63-110

3 - Introduction to Graph and Network Theory
pp 111-150

4 - Protein–Protein Interaction Data, their Quality, and Major Public Databases
pp 151-192

5 - Graphlets in Network Science and Computational Biology
pp 193-240

6 - Unsupervised Learning: Cluster Analysis
pp 241-285

7 - Machine Learning for Data Integration in Cancer Precision Medicine: Matrix Factorization Approaches
pp 286-312

8 - Machine Learning for Biomarker Discovery: Significant Pattern Mining
pp 313-368

9 - Network Alignment
pp 369-413

10 - Network Medicine
pp 414-458

11 - Elucidating Genotype-to-Phenotype Relationships via Analyses of Human Tissue Interactomes
pp 459-489

12 - Network Neuroscience
pp 490-532

13 - Cytoscape: A Tool for Analyzing and Visualizing Network Data
pp 533-592

14 - Analysis of the Signatures of Cancer Stem Cells in Malignant Tumors Using Protein Interactomes and the STRING Database
pp 593-620

Index
pp 621-632

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Analyzing Network Data in Biology and Medicine

An Interdisciplinary Textbook for Biological, Medical and Computational Scientists

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