Overview
- First book on Bayesian methods in structural bioinformatics, defining an important emerging field
- High profile contributors
- Unlike other edited volumes, the book forms a solid unity, with nearly 100 pages introductory material
- Provides a complete "starter kit" to the field -Suitable for teaching
Part of the book series: Statistics for Biology and Health (SBH)
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About this book
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.
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Keywords
Table of contents (13 chapters)
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Front Matter
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Energy Functions for Protein Structure Prediction
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Front Matter
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Directional Statistics for Biomolecular Structure
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Front Matter
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Directional statistics for biomolecular structure
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Shape Theory for Protein Structure Superposition
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Front Matter
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Shape theory for protein structure superposition
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Graphical Models for Structure Prediction
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Front Matter
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Graphical models for structure prediction
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Inferring Structure from Experimental Data
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Front Matter
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Inferring structure from experimental data
Editors and Affiliations
About the editors
Thomas Hamelryck is an associate professor at the Bioinformatics Center, University of Copenhagen. He completed his PhD in macromolecular crystallography at the Free University of Brussels (VUB). His research interests include the application of Bayesian machine learning methods and directional statistics to the inference of protein and RNA structure, based on sequence information or experimental data.
Kanti Mardia (Senior Research Professor, University of Leeds) is a pioneering researcher and leader in modern statistical science, and is responsible for numerous groundbreaking developments; his monographs are highly acclaimed and he has played a lasting leadership role in interdisciplinary research. His most outstanding contributions lie in directional data analysis, shape analysis, spatial statistics, multivariate analysis, and protein bioinformatics.
Jesper Ferkinghoff-Borg is an associate professor at the section for Biomedical Engineering, DTU-Electro, Technical University of Denmark (DTU), Copenhagen, where he heads the computational biophysics group. He received his PhD in theoretical physics from the Niels Bohr Institute at the University of Copenhagen.Â
Bibliographic Information
Book Title: Bayesian Methods in Structural Bioinformatics
Editors: Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg
Series Title: Statistics for Biology and Health
DOI: https://doi.org/10.1007/978-3-642-27225-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-27224-0Published: 26 March 2012
Softcover ISBN: 978-3-642-43988-9Published: 13 April 2014
eBook ISBN: 978-3-642-27225-7Published: 23 March 2012
Series ISSN: 1431-8776
Series E-ISSN: 2197-5671
Edition Number: 1
Number of Pages: XXII, 386
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Molecular Medicine, Biological and Medical Physics, Biophysics, Mathematical and Computational Biology, Computational Biology/Bioinformatics