biological network analysis

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Biological Network Analysis
Author : Pietro Hiram Guzzi,Swarup Roy
Publisher : Elsevier
Release Date : 2020-05-11
ISBN 10 : 0128193514
Pages : 210 pages
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Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes Includes a discussion of various graph theoretic and data analytics approaches

Analysis of Biological Networks
Author : Björn H. Junker,Falk Schreiber
Publisher : John Wiley & Sons
Release Date : 2011-09-20
ISBN 10 : 1118209915
Pages : 368 pages
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An introduction to biological networks and methods for theiranalysis Analysis of Biological Networks is the first book of itskind to provide readers with a comprehensive introduction to thestructural analysis of biological networks at the interface ofbiology and computer science. The book begins with a brief overviewof biological networks and graph theory/graph algorithms and goeson to explore: global network properties, network centralities,network motifs, network clustering, Petri nets, signal transductionand gene regulation networks, protein interaction networks,metabolic networks, phylogenetic networks, ecological networks, andcorrelation networks. Analysis of Biological Networks is a self-containedintroduction to this important research topic, assumes no expertknowledge in computer science or biology, and is accessible toprofessionals and students alike. Each chapter concludes with asummary of main points and with exercises for readers to test theirunderstanding of the material presented. Additionally, an FTP sitewith links to author-provided data for the book is available fordeeper study. This book is suitable as a resource for researchers in computerscience, biology, bioinformatics, advanced biochemistry, and thelife sciences, and also serves as an ideal reference text forgraduate-level courses in bioinformatics and biologicalresearch.

Weighted Network Analysis
Author : Steve Horvath
Publisher : Springer Science & Business Media
Release Date : 2011-04-30
ISBN 10 : 9781441988195
Pages : 421 pages
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High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.

Computational Network Analysis with R
Author : Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib
Publisher : John Wiley & Sons
Release Date : 2016-07-22
ISBN 10 : 3527694404
Pages : 368 pages
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This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Thermodynamic Network Analysis of Biological Systems
Author : J. Schnakenberg
Publisher : Springer Science & Business Media
Release Date : 2012-12-06
ISBN 10 : 3642679714
Pages : 150 pages
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The first edition of this book was greeted with broad interest from readers en gaged in various disciplines of biophysics. I received many stimulating and en couraging responses, however, some of the book's reviewers wanted to stress the fact that an extensive literature of network theory was not included or reported in the book. But the main aspect of the book is intended to be substantive rather than methodical: networks simply serve as a remedy for doing some first steps in analysing and modelling complex biological systems. For an advanced stage in the investigation of a particular system it may be appropriate to replace the pheno menological network method by more detailed techniques like statistical equations or computer simulations. According to this intention, the second edition of the book has been enlarged by further biological examples for network analysis, not by more network theory. There is a completely new section on a network model for photoreception. For this section I am obliged to J. Tiedge who did most of the detailed calculation and to my colleague Professor Stieve with whom we have had a very fruitful cooperation. Also I would like to mention that this work has been sponsored by the "Deutsche Forschungsgemei nschaft" i n the "Sonderforschungsberei ch 160". Recent results for excitable systems represented by feedback networks have also been included in the second edition, especially for limit cycle networks.

Statistical and Evolutionary Analysis of Biological Networks
Author : Michael P. H. Stumpf,Carsten Wiuf
Publisher : World Scientific
Release Date : 2010
ISBN 10 : 1848164335
Pages : 170 pages
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Networks provide a very useful way to describe a wide range of different data types in biology, physics and elsewhere. Apart from providing a convenient tool to visualize highly dependent data, networks allow stringent mathematical and statistical analysis. In recent years, much progress has been achieved to interpret various types of biological network data such as transcriptomic, metabolomic and protein interaction data as well as epidemiological data. Of particular interest is to understand the organization, complexity and dynamics of biological networks and how these are influenced by network evolution and functionality. This book reviews and explores statistical, mathematical and evolutionary theory and tools in the understanding of biological networks. The book is divided into comprehensive and self-contained chapters, each of which focuses on an important biological network type, explains concepts and theory and illustrates how these can be used to obtain insight into biologically relevant processes and questions. There are chapters covering metabolic, transcriptomic, protein interaction and epidemiological networks as well as chapters that deal with theoretical and conceptual material. The authors, who contribute to the book, are active, highly regarded and well-known in the network community.

Recent Advances in Biological Network Analysis
Author : Byung-Jun Yoon,Xiaoning Qian
Publisher : Springer
Release Date : 2021-03-10
ISBN 10 : 9783030571726
Pages : 217 pages
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This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights.

Introduction to Biological Networks
Author : Alpan Raval,Animesh Ray
Publisher : CRC Press
Release Date : 2016-04-19
ISBN 10 : 1420010360
Pages : 335 pages
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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

Analyzing Network Data in Biology and Medicine
Author : Nata a Pr ulj,Nataša Pržulj
Publisher : Cambridge University Press
Release Date : 2019-03-28
ISBN 10 : 1108432239
Pages : 672 pages
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Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.

Applied Statistics for Network Biology
Author : Matthias Dehmer,Frank Emmert-Streib,Armin Graber,Armindo Salvador
Publisher : John Wiley & Sons
Release Date : 2011-04-08
ISBN 10 : 9783527638086
Pages : 478 pages
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The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.

Biological Networks and Pathway Analysis
Author : Tatiana V. Tatarinova,Yuri Nikolsky
Publisher : Humana Press
Release Date : 2017-08-29
ISBN 10 : 9781493970254
Pages : 509 pages
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In this volume, expert practitioners present a compilation of methods of functional data analysis (often referred to as “systems biology”) and its applications in drug discovery, medicine, and basic disease research. It covers such important issues as the elucidation of protein, compound and gene interactions, as well as analytical tools, including networks, interactome and ontologies, and clinical applications of functional analysis. As a volume in the highly successful Methods in Molecular Biology series, this work provides detailed description and hands-on implementation advice. Reputable, comprehensive, and cutting-edge, Biological Networks and Pathway Analysis presents both “wet lab” experimental methods and computational tools in order to cover a broad spectrum of issues in this fascinating new field.

Network Biology
Author : Intawat Nookaew
Publisher : Springer
Release Date : 2017-05-03
ISBN 10 : 3319564609
Pages : 202 pages
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This book review series presents current trends in modern biotechnology. The aim is to cover all aspects of this interdisciplinary technology where knowledge, methods and expertise are required from chemistry, biochemistry, microbiology, genetics, chemical engineering and computer science. Volumes are organized topically and provide a comprehensive discussion of developments in the respective field over the past 3-5 years. The series also discusses new discoveries and applications. Special volumes are dedicated to selected topics which focus on new biotechnological products and new processes for their synthesis and purification. In general, special volumes are edited by well-known guest editors. The series editor and publisher will however always be pleased to receive suggestions and supplementary information. Manuscripts are accepted in English.

Artificial Neural Networks in Biological and Environmental Analysis
Author : Grady Hanrahan
Publisher : CRC Press
Release Date : 2011-01-18
ISBN 10 : 9781439812594
Pages : 214 pages
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Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and environmental processes. Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental, technological, and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field, the book stimulates communication and partnership among scientists in fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers: A brief history of computational neural network models in relation to brain function Neural network operations, including neuron connectivity and layer arrangement Basic building blocks of model design, selection, and application from a statistical perspective Neurofuzzy systems, neuro-genetic systems, and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes Scientists deal with very complicated systems, much of the inner workings of which are frequently unknown to researchers. Using only simple, linear mathematical methods, information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed, and ANNs can play a major role. Balancing basic principles and diverse applications, this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.

Biological Network Analysis
Author : Pietro Hiram Guzzi,Swarup Roy
Publisher : Elsevier
Release Date : 2020-05-11
ISBN 10 : 0128193514
Pages : 210 pages
GET BOOK!

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes Includes a discussion of various graph theoretic and data analytics approaches

Biological Networks
Author : Francois Kepes
Publisher : World Scientific
Release Date : 2007
ISBN 10 : 9812772367
Pages : 516 pages
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This volume presents a timely and comprehensive overview of biological networks at all organization levels in the spirit of the complex systems 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. Sample Chapter(s). Chapter 1: Scale-Free Networks in Biology (821 KB). Contents: Scale-Free Networks in Biology (E Almaas et al.); Modularity in Biological Networks (R V Sol(r) et al.); Inference of Biological Regulatory Networks: Machine Learning Approaches (F d''Alch(r)-Buc); Transcriptional Networks (F K(r)p s); Protein Interaction Networks (K Tan & T Ideker); Metabolic Networks (D A Fell); Heterogeneous Molecular Networks (V Schnchter); Evolution of Regulatory Networks (A Veron et al.); Complexity in Neuronal Networks (Y Fr(r)gnac et al.); Networks of the Immune System (R E Callard & J Stark); A History of the Study of Ecological Networks (L-F Bersier); Dynamic Network Models of Ecological Diversity, Complexity, and Nonlinear Persistence (R J Williams & N D Martinez); Infection Transmission through Networks (J S Koopman). Readership: Graduate students and industry experts in systems biology and complex systems; biologists; chemists; physicists; mathematicians; computer scientists

Methods in Bioengineering
Author : Arul Jayaraman,Juergen Hahn
Publisher : Artech House
Release Date : 2009
ISBN 10 : 1596934069
Pages : 316 pages
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"This cutting-edge volume provides a detailed look at the two main aspects of systems biology: the design of sophisticated experimental methods and the development of complex models to analyze the data. Focusing on methods that are being used to solve current problems in biomedical science and engineering, this comprehensive, richly illustrated resource shows you how to: design of state-of-the art methods for analyzing biological systems Implement experimental approaches for investigating cellular behavior in health and disease; use algorithms and modeling techniques for quantitatively describing biomedical problems; and integrate experimental and computational approaches for a more complete view of biological systems." --Book Jacket.

Knowledge Discovery in Proteomics
Author : Igor Jurisica,Dennis Wigle
Publisher : CRC Press
Release Date : 2005-09-02
ISBN 10 : 1420035169
Pages : 360 pages
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Multi-modal representations, the lack of complete and consistent domain theories, rapid evolution of domain knowledge, high dimensionality, and large amounts of missing information - these are challenges inherent in modern proteomics. As our understanding of protein structure and function becomes ever more complicated, we have reached a point where

The Structure of Complex Networks
Author : Ernesto Estrada
Publisher : Oxford University Press
Release Date : 2012
ISBN 10 : 019959175X
Pages : 465 pages
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The book integrates approaches from mathematics, physics and computer sciences to analyse the organisation of complex networks. Every organisational principle of networks is defined, quantified and then analysed for its influences on the properties and functions of molecular, biological, ecological and social networks.

Protein Networks and Pathway Analysis
Author : Yuri Nikolsky,Julie Bryant
Publisher : Humana Press
Release Date : 2011-11-30
ISBN 10 : 9781617794889
Pages : 410 pages
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From the beginning of the OMICs biology era, science has been pursuing the reduction of the complex "genome-wide" assays in order to understand the essential biology that lies beneath it. In Protein Networks and Pathway Analysis, expert practitioners present a compilation of methods of functional data analysis, often referred to as "systems biology," and its applications in drug discovery, medicine and basic disease research. The volume is divided into three convenient sections, covering the elucidation of protein, compound and gene interactions, analytical tools, including networks, interactome and ontologies, and applications of functional analysis. As a volume in the highly successful Methods in Molecular BiologyTM series, this work provides detailed descriptions and hands-on implementation advice. Authoritative and cutting-edge, Protein Networks and Pathway Analysis presents both "wet lab" experimental methods and computational tools in order to cover a broad spectrum of issues in this fascinating new field.

Clustering Challenges in Biological Networks
Author : Sergiy Butenko
Publisher : World Scientific
Release Date : 2009
ISBN 10 : 9812771662
Pages : 334 pages
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This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems.