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Bayesian Methods in Structural Bioinformatics

Bayesian Methods in Structural Bioinformatics
Author: Thomas Hamelryck
Publisher: Springer
Total Pages: 399
Release: 2012-03-23
Genre: Medical
ISBN: 3642272258

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This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.


Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics
Author: Dipak K. Dey
Publisher: CRC Press
Total Pages: 466
Release: 2010-09-03
Genre: Mathematics
ISBN: 1420070185

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Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c


Advance in Structural Bioinformatics

Advance in Structural Bioinformatics
Author: Dongqing Wei
Publisher: Springer
Total Pages: 380
Release: 2014-11-11
Genre: Science
ISBN: 9401792453

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This text examines in detail mathematical and physical modeling, computational methods and systems for obtaining and analyzing biological structures, using pioneering research cases as examples. As such, it emphasizes programming and problem-solving skills. It provides information on structure bioinformatics at various levels, with individual chapters covering introductory to advanced aspects, from fundamental methods and guidelines on acquiring and analyzing genomics and proteomics sequences, the structures of protein, DNA and RNA, to the basics of physical simulations and methods for conformation searches. This book will be of immense value to researchers and students in the fields of bioinformatics, computational biology and chemistry. Dr. Dongqing Wei is a Professor at the Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiaotong University, Shanghai, China. His research interest is in the general area of structural bioinformatics.


Bayesian Inference for Gene Expression and Proteomics

Bayesian Inference for Gene Expression and Proteomics
Author: Kim-Anh Do
Publisher: Cambridge University Press
Total Pages: 437
Release: 2006-07-24
Genre: Mathematics
ISBN: 052186092X

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Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.


Structural Bioinformatics

Structural Bioinformatics
Author: Zoltán Gáspári
Publisher: Humana
Total Pages: 256
Release: 2021-02-15
Genre: Science
ISBN: 9781071602720

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This volume looks at the latest techniques used to perform comparative structure analyses, and predict and evaluate protein-ligand interactions. The chapters in this book cover tools and servers such as LiteMol; Bio3D-Web; DALI; CATH; HoTMuSiC, a contact-base protein structure analysis tool known as CAD-Score; PyDockSaxs and HADDOCK; CombDock and DockStar; the BioMagResBank database; as well as BME and CoNSEnsX+. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Structural Bioinformatics: Methods and Protocols is a practical guide for researchers to learn more about the aforementioned tools to further enhance their studies in the growing field of structural bioinformatics. Chapter 13 is available open access under a CC-BY 4.0 license via link.springer.com.


Bayesian Analysis of Gene Expression Data

Bayesian Analysis of Gene Expression Data
Author: Bani K. Mallick
Publisher: John Wiley & Sons
Total Pages: 252
Release: 2009-07-20
Genre: Mathematics
ISBN: 9780470742815

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The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable. This book: Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.


Structural Bioinformatics

Structural Bioinformatics
Author: Forbes J. Burkowski
Publisher: CRC Press
Total Pages: 444
Release: 2008-10-30
Genre: Computers
ISBN: 1420011790

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The Beauty of Protein Structures and the Mathematics behind Structural BioinformaticsProviding the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure.Helps Students Go Further in Their Study of Structural Biolog


Structural Bioinformatics

Structural Bioinformatics
Author: Jenny Gu
Publisher: John Wiley & Sons
Total Pages: 1105
Release: 2011-09-20
Genre: Science
ISBN: 1118210565

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Structural Bioinformatics was the first major effort to show the application of the principles and basic knowledge of the larger field of bioinformatics to questions focusing on macromolecular structure, such as the prediction of protein structure and how proteins carry out cellular functions, and how the application of bioinformatics to these life science issues can improve healthcare by accelerating drug discovery and development. Designed primarily as a reference, the first edition nevertheless saw widespread use as a textbook in graduate and undergraduate university courses dealing with the theories and associated algorithms, resources, and tools used in the analysis, prediction, and theoretical underpinnings of DNA, RNA, and proteins. This new edition contains not only thorough updates of the advances in structural bioinformatics since publication of the first edition, but also features eleven new chapters dealing with frontier areas of high scientific impact, including: sampling and search techniques; use of mass spectrometry; genome functional annotation; and much more. Offering detailed coverage for practitioners while remaining accessible to the novice, Structural Bioinformatics, Second Edition is a valuable resource and an excellent textbook for a range of readers in the bioinformatics and advanced biology fields. Praise for the previous edition: "This book is a gold mine of fundamental and practical information in an area not previously well represented in book form." —Biochemistry and Molecular Education "... destined to become a classic reference work for workers at all levels in structural bioinformatics...recommended with great enthusiasm for educators, researchers, and graduate students." —BAMBED "...a useful and timely summary of a rapidly expanding field." —Nature Structural Biology "...a terrific job in this timely creation of a compilation of articles that appropriately addresses this issue." —Briefings in Bioinformatics


Geometry Driven Statistics

Geometry Driven Statistics
Author: Ian L. Dryden
Publisher: John Wiley & Sons
Total Pages: 432
Release: 2015-07-22
Genre: Mathematics
ISBN: 1118866614

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A timely collection of advanced, original material in the area of statistical methodology motivated by geometric problems, dedicated to the influential work of Kanti V. Mardia This volume celebrates Kanti V. Mardia's long and influential career in statistics. A common theme unifying much of Mardia’s work is the importance of geometry in statistics, and to highlight the areas emphasized in his research this book brings together 16 contributions from high-profile researchers in the field. Geometry Driven Statistics covers a wide range of application areas including directional data, shape analysis, spatial data, climate science, fingerprints, image analysis, computer vision and bioinformatics. The book will appeal to statisticians and others with an interest in data motivated by geometric considerations. Summarizing the state of the art, examining some new developments and presenting a vision for the future, Geometry Driven Statistics will enable the reader to broaden knowledge of important research areas in statistics and gain a new appreciation of the work and influence of Kanti V. Mardia.


Probabilistic Methods for Bioinformatics

Probabilistic Methods for Bioinformatics
Author: Richard E. Neapolitan
Publisher: Morgan Kaufmann
Total Pages: 421
Release: 2009-06-12
Genre: Computers
ISBN: 0080919367

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The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis. Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics. Shares insights about when and why probabilistic methods can and cannot be used effectively; Complete review of Bayesian networks and probabilistic methods with a practical approach.