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The analysis of protein-protein interactions is fundamental to the understanding of cellular organization, processes, and functions. Recent large-scale investigations of protein-protein interactions using such techniques as two-hybrid systems, mass spectrometry, and protein microarrays have enriched the available protein interaction data and facilitated the construction of integrated protein-protein interaction networks. The resulting large volume of protein-protein interaction data has posed a challenge to experimental investigation. This book provides a comprehensive understanding of the computational methods available for the analysis of protein-protein interaction networks. It offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods. The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research.Read more
- Offers first comprehensive survey of the many computational methods available for the analysis of protein-protein interaction networks
- Includes information on the most cutting-edge research and state of the art approaches in the field
Reviews & endorsements
"Up-to-date on the research and thoroughly comprehensive in coverage, Aidong Zhang’s Protein Interaction Networks: Computational Analysis is an invaluable contribution to our understanding and knowledge of current analytic methods for protein interaction networks. Written with technical depth and sophistication, and replete with examples, this book will be both an indispensable manual for practitioners and a crucial textbook for teaching."
Jiawei Han, Professor of Computer Science, University of Illinois at Urbana-ChampaignSee more reviews
"This book provides a comprehensive coverage of current research issues and solutions in protein interaction networks. Within this new and exciting area of research, certain topics are explored in depth, including newest results reported by the author and other leading experts. I highly recommend this book for researchers and students who are interested in bioinformatics."
Yi Pan, Chair and Professor of Computer Science, Georgia State University
"This book provides the most comprehensive and systematic review to an important biomedical research topic (protein interaction network). It gives its readers an opportunity to easily learn about this challenging topic and to begin investigating how they may contribute to it. Its great value makes it suitable for a broad range of readers, from students to experienced researchers."
Dong Xu, Professor and Chair of the Computer Science Department, University of Missouri, Columbia
"This book is a comprehensive and an excellent introduction to network biology in general and proteins networks in particular. It provides detailed description of the major computational concepts and their applications in systems biology. It is a must have book for anyone interested in this exciting topic."
Mohammed J. Zaki, Professor of Computer Science, Rensselaer Polytechnic Institute
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- Date Published: April 2009
- format: Hardback
- isbn: 9780521888950
- length: 292 pages
- dimensions: 260 x 180 x 21 mm
- weight: 0.75kg
- contains: 79 b/w illus. 11 colour illus. 30 tables
Table of Contents
2. Experimental approaches to generation of protein-protein interaction data
3. Computational methods for the prediction of protein-protein interactions
4. Basic properties and measurements of protein interaction networks
5. Modularity analysis of protein interaction networks
6. Topological analysis of protein interaction networks Woo-chang Hwang
7. Distance-based modularity analysis
8. Graph-theoretic approaches to modularity analysis
9. Flow-based analysis of protein interaction networks
10. Statistics and machine learning based analysis of protein interaction networks Pritam Chanda and Lei Shi
11. Integration of gene ontology into the analysis of protein interaction networks Young-rae Cho
12. Data fusion in the analysis of protein interaction networks
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