Bayesian Methods in Structural Bioinformatics

✍ Scribed by Thomas Hamelryck (auth.), Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg (eds.)

Publisher: Springer-Verlag Berlin Heidelberg
Year: 2012
Tongue: English
Leaves: 398
Series: Statistics for Biology and Health
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxii
Front Matter....Pages 1-1
An Overview of Bayesian Inference and Graphical Models....Pages 3-48
Monte Carlo Methods for Inference in High-Dimensional Systems....Pages 49-93
Front Matter....Pages 95-95
On the Physical Relevance and Statistical Interpretation of Knowledge-Based Potentials....Pages 97-124
Towards a General Probabilistic Model of Protein Structure: The Reference Ratio Method....Pages 125-134
Inferring Knowledge Based Potentials Using Contrastive Divergence....Pages 135-155
Front Matter....Pages 157-157
Statistics of Bivariate von Mises Distributions....Pages 159-178
Statistical Modelling and Simulation Using the Fisher-Bingham Distribution....Pages 179-188
Front Matter....Pages 189-189
Likelihood and Empirical Bayes Superposition of Multiple Macromolecular Structures....Pages 191-208
Bayesian Hierarchical Alignment Methods....Pages 209-230
Front Matter....Pages 231-231
Probabilistic Models of Local Biomolecular Structure and Their Applications....Pages 233-254
Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields....Pages 255-284
Front Matter....Pages 285-285
Inferential Structure Determination from NMR Data....Pages 287-311
Bayesian Methods in SAXS and SANS Structure Determination....Pages 313-342
Back Matter....Pages 343-385

✦ Subjects

Statistics for Life Sciences, Medicine, Health Sciences;Molecular Medicine;Biophysics and Biological Physics;Mathematical and Computational Biology;Computational Biology/Bioinformatics

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