Summarizing Biological Networks

This book focuses on the data mining, systems biology, and bioinformatics computational methods that can be used to summarize biological networks. Specifically, it discusses an array of techniques related to biological network clustering, network summarization, and differential network analysis which enable readers to uncover the functional and topological organization hidden in a large biological network. The authors also examine crucial open research problems in this arena. Academics, researchers, and advanced-level students will find this book to be a comprehensive and exceptional resource for understanding computational techniques and their applications for a summary of biological networks.

An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.

This volume presents a timely and comprehensive overview of biological networks at all organization levels in the spirit of the complex system approach. It discusses the transversal issues and fundamental principles as well as the overall structure, dynamics, and modeling of a wide array of biological networks at the molecular, cellular, and population levels. Anchored in both empirical data and a strong theoretical background, the book therefore lends valuable credence to the complex systems approach.

This book presents methods and tools of network biology and bioinformatics for understanding the disease dynamics and identification of drug targets. The initial section of chapters introduce the theoretical aspects followed by the different applications for construction and analysis of biological networks, methods for identifying crucial nodes in networks, and network dynamics. The book covers the latest advances in the network medicine, exploring the different types of biological networks, and their applications. It further reviews the role of R language in the network-based approaches that help in understanding biological systems and identifying biological functions. Towards the end, the book explores the recent developments and applications in machine learning and its potential for advancing network biology. Finally, the book elucidates a comprehensive yet a representative description of challenges associated with the understanding of disease dynamics using network biology. Given its scope, the book is intended for researchers and advanced postgraduate students of bioinformatics, computational biology, and medical sciences. ​

Molecular networks provide descriptions of the organization of various biological processes, including cellular signaling, metabolism, and genetic regulation. Knowledge on molecular networks is commonly used for systems level analysis of biological function; research and method development in this area has grown tremendously in the past few years. This book will provide a detailed review of existing knowledge on the functional characterization of biological networks. In 15 chapters authored by an international group of prolific systems biology and bioinformatics researchers, it will organize, conceptualize, and summarize the existing core of research results and computational methods on understanding biological function from a network perspective.

This two volume set LNCS 7238 and LNCS 7239 constitutes the refereed proceedings of the 17th International Conference on Database Systems for Advanced Applications, DASFAA 2012, held in Busan, South Korea, in April 2012. The 44 revised full papers and 8 short papers presented together with 2 invited keynote papers, 8 industrial papers, 8 demo presentations, 4 tutorials and 1 panel paper were carefully reviewed and selected from a total of 159 submissions. The topics covered are query processing and optimization, data semantics, XML and semi-structured data, data mining and knowledge discovery, privacy and anonymity, data management in the Web, graphs and data mining applications, temporal and spatial data, top-k and skyline query processing, information retrieval and recommendation, indexing and search systems, cloud computing and scalability, memory-based query processing, semantic and decision support systems, social data, data mining.

Networked systems are all around us. The accumulated evidence of systems as complex as a cell cannot be fully understood by studying only their isolated constituents, giving rise to a new area of interest in research ? the study of complex networks. In a broad sense, biological networks have been one of the most studied networks, and the field has benefited from many important contributions. By understanding and modeling the structure of a biological network, a better perception of its dynamical and functional behavior is to be expected. This unique book compiles the most relevant results and novel insights provided by network theory in the biological sciences, ranging from the structure and dynamics of the brain to cellular and protein networks and to population-level biology.

The new research area of genomics-inspired network biology lacks an introductory book that enables both physical/computational scientists and biologists to obtain a general yet sufficiently rigorous perspective of current thinking. Filling this gap, Introduction to Biological Networks provides a thorough introduction to genomics-inspired network bi