Analyzing Network Data in Biology and Medicine
An Interdisciplinary Textbook for Biological, Medical and Computational Scientists
- Editor: Nataša Pržulj, University College London and Barcelona Supercomputing Center
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.Read more
- Introduces graph and network theory, as well as some commonly used machine learning methods and their applications to analyze complex heterogeneous data sets
- Covers a wide array of topics from using personalized genetic tests to predicting disease risks, analysis of epigenetic and disease data, as well as protein interaction and -omics data
- Includes examples from current hot topics, such as network neuroscience and network medicine
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- Date Published: March 2019
- format: Adobe eBook Reader
- isbn: 9781108389846
- contains: 196 b/w illus. 42 tables
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. From genetic data to medicine: from DNA samples to disease risk prediction in personalized genetic tests Luis Leal, Rok Košir and Nataša Pržulj
2. Epigenetic data and disease Rodrigo González-Barrios, Marisol Salgado-Albarrán, Nicolás Alcaraz, Cristian Arriaga-Canon, Lissania Guerra-Calderas, Laura Contreras-Espinoza and Ernesto Soto-Reyes
3. Introduction to graph and network theory Thomas Gaudelet and Nataša Pržulj
4. Protein-protein interaction data, their quality, and major public databases Anne-Christin Hauschild, Chiara Pastrello, Max Kotlyar and Igor Jurisica
5. Graphlets in network science and computational biology Khalique Newaz and Tijana Milenković
6. Cluster analysis Richard Röttger
7. Machine learning for data integration in cancer precision medicine: matrix factorization approaches Noël Malod-Dognin, Sam Windels and Nataša Pržulj
8. Machine learning for biomarker discovery: significant pattern mining F. Llinares-Lopez and K. Borgwardt
9. Network alignment Noël Malod-Dogning and Nataša Pržulj
10. Network medicine Pisanu Buphamalai, Michael Caldera, Felix Müller and Jörg Menche
11. Elucidating genotype-to-phenotype relationships via analyzes of human tissue interactomes Idan Hekselman, Moran Sharon, Omer Basha and Esti Yeger-Lotem
12. Network neuroscience Alberto Cacciola, Alessandro Muscoloni and Carlo Vittorio Cannistraci
13. Cytoscape: tool for analyzing and visualizing network data John H. Morris
14. Analysis of the signatures of cancer stem cells in malignant tumours using protein interactomes and STRING database Krešimir Pavelić, Marko Klobučar, Dolores Kuzelj, Nataša Pržulj and Sandra Kraljević Pavelić.
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