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Weighted Network Analysis

Author: Steve Horvath
Publisher: Springer Science & Business Media
ISBN: 9781441988195
Size: 61.66 MB
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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.

Genomics Circuits And Pathways In Clinical Neuropsychiatry

Author: Thomas Lehner
Publisher: Academic Press
ISBN: 0128005300
Size: 80.89 MB
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This foundational work comprehensively examines the current state of the genetics, genomics and brain circuitry of psychiatric and neurological disorders. It consolidates discoveries of specific genes and genomic regions associated with these conditions, the genetic and anatomic architecture of these syndromes, and addresses how recent advances in genomics are leading to a reappraisal of the biology underlying clinical neuroscience. In doing so, it critically examines the promise and limitations of these discoveries toward treatment, and to the interdisciplinary nature of understanding brain and behavior. Coverage includes new discoveries regarding autism, epilepsy, intellectual disability, dementias, movement disorders, language impairment, disorders of attention, schizophrenia, and bipolar disorder. Genomics, Circuits, and Pathways in Clinical Neuropsychiatry focuses on key concepts, challenges, findings, and methods in genetics, genomics, molecular pathways, brain circuitry, and related neurobiology of neurologic and psychiatric disorders. Provides interdisciplinary appeal in psychiatry, neurology, neuroscience, and genetics Identifies key concepts, methods, and findings Includes coverage of multiple disorders from autism to schizophrenia Reviews specific genes associated with disorders Discusses the genetic architecture of these syndromes Explains how recent findings are influencing the understanding of biology Clarifies the promise of these findings for future treatment

Social Network Analysis Applied To Team Sports Analysis

Author: Filipe Manuel Clemente
Publisher: Springer
ISBN: 3319258559
Size: 60.25 MB
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Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.

Quantitative Biology

Author: Brian Munsky
Publisher: MIT Press
ISBN: 0262347113
Size: 32.13 MB
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An introduction to the quantitative modeling of biological processes, presenting modeling approaches, methodology, practical algorithms, software tools, and examples of current research. The quantitative modeling of biological processes promises to expand biological research from a science of observation and discovery to one of rigorous prediction and quantitative analysis. The rapidly growing field of quantitative biology seeks to use biology's emerging technological and computational capabilities to model biological processes. This textbook offers an introduction to the theory, methods, and tools of quantitative biology. The book first introduces the foundations of biological modeling, focusing on some of the most widely used formalisms. It then presents essential methodology for model-guided analyses of biological data, covering such methods as network reconstruction, uncertainty quantification, and experimental design; practical algorithms and software packages for modeling biological systems; and specific examples of current quantitative biology research and related specialized methods. Most chapters offer problems, progressing from simple to complex, that test the reader's mastery of such key techniques as deterministic and stochastic simulations and data analysis. Many chapters include snippets of code that can be used to recreate analyses and generate figures related to the text. Examples are presented in the three popular computing languages: Matlab, R, and Python. A variety of online resources supplement the the text. The editors are long-time organizers of the Annual q-bio Summer School, which was founded in 2007. Through the school, the editors have helped to train more than 400 visiting students in Los Alamos, NM, Santa Fe, NM, San Diego, CA, Albuquerque, NM, and Fort Collins, CO. This book is inspired by the school's curricula, and most of the contributors have participated in the school as students, lecturers, or both. Contributors John H. Abel, Roberto Bertolusso, Daniela Besozzi, Michael L. Blinov, Clive G. Bowsher, Fiona A. Chandra, Paolo Cazzaniga, Bryan C. Daniels, Bernie J. Daigle, Jr., Maciej Dobrzynski, Jonathan P. Doye, Brian Drawert, Sean Fancer, Gareth W. Fearnley, Dirk Fey, Zachary Fox, Ramon Grima, Andreas Hellander, Stefan Hellander, David Hofmann, Damian Hernandez, William S. Hlavacek, Jianjun Huang, Tomasz Jetka, Dongya Jia, Mohit Kumar Jolly, Boris N. Kholodenko, Markek Kimmel, Michal Komorowski, Ganhui Lan, Heeseob Lee, Herbert Levine, Leslie M Loew, Jason G. Lomnitz, Ard A. Louis, Grant Lythe, Carmen Molina-París, Ion I. Moraru, Andrew Mugler, Brian Munsky, Joe Natale, Ilya Nemenman, Karol Nienaltowski, Marco S. Nobile, Maria Nowicka, Sarah Olson, Alan S. Perelson, Linda R. Petzold, Sreenivasan Ponnambalam, Arya Pourzanjani, Ruy M. Ribeiro, William Raymond, William Raymond, Herbert M. Sauro, Michael A. Savageau, Abhyudai Singh, James C. Schaff, Boris M. Slepchenko, Thomas R. Sokolowski, Petr Šulc, Andrea Tangherloni, Pieter Rein ten Wolde, Philipp Thomas, Karen Tkach Tuzman, Lev S. Tsimring, Dan Vasilescu, Margaritis Voliotis, Lisa Weber

Neuroepigenomics In Aging And Disease

Author: Raul Delgado-Morales
Publisher: Springer
ISBN: 3319538896
Size: 77.36 MB
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Epigenetic mechanisms (DNA modifications, histone alterations and non-coding RNAs) are crucial for transcriptional regulation and alterations of the “physiological epigenome” are increasingly associated with human diseases. During the last decade the emerging field of neuroepigenomics have started to impact tremendously in areas such learning and memory, addiction or neurodegeneration. This expert volume covers the role of epigenetic molecular mechanism in regulation of central nervous system’s function, one of the most exciting areas of contemporary molecular neuroscience. The book describes the current knowledge on the epigenetic basis of human disease covering the complete lifespan: from neurodevelopment/childhood (Rett Syndrome, Rubinstein-Taybi, autism), adolescence (eating disorders, drug addiction, anxiety), adulthood (depression, schizophrenia, amyotrophic lateral sclerosis, Huntington’s disease) and elderly (Alzheimer’s disease, Parkinson’s disease). The book also covers the three major players on neuroepigenomic mechanisms: histones alterations, DNA modifications and non-coding RNAs, their roles at the molecular and cellular level and the impact of their alterations on neuronal function and behavior. Finally, a special chapter on state-of-the-art technologies helps the reader not only to understand epigenetic driven changes in human cognition and diseases but also the methodology that will help to generate paradigm shifts on our understanding of brain function and the role of the neuroepigenome in human diseases.

The Omics

Author: Giovanni Coppola
Publisher: Oxford University Press
ISBN: 0199355002
Size: 26.35 MB
Format: PDF, Kindle
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The OMICs: Applications in Neuroscience summarizes the state of the art in high-throughput approaches (collectively known as 'OMICs') in neurology and neuroscience, and is of interest to both neurologists tracking the progress of these methods towards clinical applications, and neuroscientists curious about the most recent advances in this ever-changing field. The explosion of high-throughput assays has introduced large datasets, computational servers, and bioinformatics approaches to neuroscience, and medicine in general. The book includes a rich survey of the most relevant OMICs applications and how they relate to neurology and neuroscience. The reader is given an overview of the method, a perspective on the current and future applications, and published examples illustrating practical uses.

Biomolecular Networks

Author: Luonan Chen
Publisher: John Wiley & Sons
ISBN: 9780470488058
Size: 65.71 MB
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Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.

Bioinformatics Genomics And Proteomics

Author: Ruchi Singh
Publisher: Vikas Publishing House
ISBN: 9325978555
Size: 15.58 MB
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This is an innovative textbook for undergraduates as well as postgraduates offering basic knowledge of biology. Its aim is to provide state-of-the-art information about this developing science that has the potential to replace existing biological approaches to study genes and proteins. The chapters are explained in a concise yet detailed manner, including ample cross-references, references to literature and databases, tables and illustrations. The book's sound approach to this intricately complex field makes it an exceptional resource for further exploration into biochemistry, molecular biology, genomics and drug designing fields. Abundant learning features make this book the ideal teaching and learning tool. KEY FEATURES • Illustrations to bolster understanding of complex biochemical relations • Tables for quick access to precise data • Extensive end-of-chapter exercises and references • The most basic details furnished for those who are new to biology • User-friendly, Internet-based bioinformatics tools that allow researchers to extract information from databases and analyze it • Analysis of one software tool discussed in each chapter step-by-step from entering the input till interpretation of the results This is an in-depth textbook written for the biologist who wants a thorough understanding of the popular bioinformatics programs and molecular databases currently in use. It provides a broad, application-oriented overview of this technology.

Statistical And Machine Learning Approaches For Network Analysis

Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 111834698X
Size: 52.75 MB
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Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Algorithms In Computational Molecular Biology

Author: Mourad Elloumi
Publisher: John Wiley & Sons
ISBN: 1118101987
Size: 70.22 MB
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This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.