Bayesian Methods in Structural Bioinformatics
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 st
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Thomas Hamelryck Kanti Mardia Jesper Ferkinghoff-Borg Editors
Bayesian Methods in Structural Bioinformatics
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Editors Thomas Hamelryck University of Copenhagen Department of Biology Bioinformatics Centre Copenhagen Denmark
Jesper Ferkinghoff-Borg Technical University of Denmark Department of Electrical Engineering Lyngby Denmark
Kanti Mardia University of Leeds School of Mathematics Department of Statistics Leeds United Kingdom Statistics for Biology and Health Series Editors M. Gail A. Tsiatis National Cancer Institute Department of Statistics Bethesda, MD North Carolina State University USA Raleigh, NC USA Klaus Krickeberg Le Chˆatelet W. Wong Manglieu Department of Statistics France Stanford University Stanford, CA Jonathan M. Samet USA Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles, CA USA
ISSN 1431-8776 ISBN 978-3-642-27224-0 e-ISBN 978-3-642-27225-7 DOI 10.1007/978-3-642-27225-7 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2012933773 c Springer-Verlag Berlin Heidelberg 2012 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Thomas Hamelryck dedicates his contributions to his mother Gib, and to the memory of his father Luc (1942–2011). Kanti Mardia dedicates his contributions to his grandson Sashin Raghunath.
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Foreword
The publication of this ground-breaking and thought-provoking book in a prestigious Springer series will be a source of particular pleasure and of stimulus for all scientists who have used Bayesian methods in their own specialized area of Bioinformatics, and of excitement for those who have wanted to understand them and learn how to use them but have never dared ask. I met the lead author, Dr. Hamelryck, at the start of his career, when, as part of his PhD in Protein Crystallography, he determined and proceeded to analyze in great detail the 3D structures of several tight protein-carbohydrate complexes. His attention was drawn to Bayesian and related statistical methods by the profound impact they were having at
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