Protein Interaction Networks: Computational Analysis
Apr 2009 · Cambridge University Press
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reportUkadiriaji na maoni hayajahakikishwa Pata Maelezo Zaidi
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The analysis of protein-protein interactions is fundamental to the understanding of cellular organization, processes, and functions. Proteins seldom act as single isolated species; rather, proteins involved in the same cellular processes often interact with each other. Functions of uncharacterized proteins can be predicted through comparison with the interactions of similar known proteins. 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.
Kuhusu mwandishi
Dr Aidong Zhang is a professor in the Department of Computer Science and Engineering at the State University of New York at Buffalo and the director of the Buffalo Center for Biomedical Computing (BCBC). She is an author of more than 190 research publications and has served on the editorial boards of the International Journal of Bioinformatics Research and Applications (IJBRA), ACM Multimedia Systems, the International Journal of Multimedia Tools and Applications, the International Journal of Distributed and Parallel Databases, and ACM SIGMOD DiSC (Digital Symposium Collection). Dr Zhang is a recipient of the National Science Foundation CAREER Award and SUNY (State University of New York) Chancellor's Research Recognition Award.
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