Analyzing Network Data in Biology and Medicine
An Interdisciplinary Textbook for Biological, Medical and Computational Scientists
£46.99
- Editor: Nataša Pržulj, University College London and Barcelona Supercomputing Center
- Date Published: March 2019
- availability: In stock
- format: Paperback
- isbn: 9781108432238
£
46.99
Paperback
Other available formats:
eBook
Looking for an inspection copy?
This title is not currently available on inspection
-
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
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: March 2019
- format: Paperback
- isbn: 9781108432238
- length: 643 pages
- dimensions: 247 x 175 x 25 mm
- weight: 1.28kg
- contains: 196 b/w illus. 42 tables
- availability: In stock
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.-
Resources for
Analyzing Network Data in Biology and Medicine
Find resources associated with this title
Type Name Unlocked * Format Size Showing of
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact [email protected].
Related Books
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email [email protected]
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×