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Tutorial on biological networks
Author(s) -
VitalLopez Francisco G.,
Memišević Vesna,
Dutta Bhaskar
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1061
Subject(s) - biological network , data science , context (archaeology) , systems biology , computer science , biological data , network analysis , field (mathematics) , modelling biological systems , network science , visualization , management science , complex network , computational biology , artificial intelligence , biology , world wide web , bioinformatics , engineering , paleontology , mathematics , pure mathematics , electrical engineering
Understanding how the functioning of a biological system emerges from the interactions among its components is a long‐standing goal of network science. Fomented by developments in high‐throughput technologies to characterize biomolecules and their interactions, network science has emerged as one of the fastest growing areas in computational and systems biology research. Although the number of research and review articles on different aspects of network science is increasing, updated resources that provide a broad, yet concise, review of this area in the context of systems biology are few. The objective of this article is to provide an overview of the research on biological networks to a general audience, who have some knowledge of biology and statistics, but are not necessarily familiar with this research field. Based on the different aspects of network science research, the article is broadly divided into four sections: (1) network construction, (2) topological analysis, (3) network and data integration, and (4) visualization tools. We specifically focused on the most widely studied types of biological networks, which are, metabolic, gene regulatory, protein–protein interaction, genetic interaction, and signaling networks. In future, with further developments on experimental and computational methods, we expect that the analysis of biological networks will assume a leading role in basic and translational research. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Data Mining Software Tools Application Areas > Science and Technology