Bayesian Methods in Structural Bioinformatics

  • Thomas Hamelryck
  • Kanti Mardia
  • Jesper Ferkinghoff-Borg
Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Foundations

    1. Front Matter
      Pages 1-1
  3. Energy Functions for Protein Structure Prediction

    1. Front Matter
      Pages 95-95
  4. Energy functions for protein structure prediction

    1. Mikael Borg, Thomas Hamelryck, Jesper Ferkinghoff-Borg
      Pages 97-124
    2. Jes Frellsen, Kanti V. Mardia, Mikael Borg, Jesper Ferkinghoff-Borg, Thomas Hamelryck
      Pages 125-134
    3. Alexei A. Podtelezhnikov, David L. Wild
      Pages 135-155
  5. Directional Statistics for Biomolecular Structure

    1. Front Matter
      Pages 157-157
  6. Directional statistics for biomolecular structure

  7. Shape Theory for Protein Structure Superposition

    1. Front Matter
      Pages 189-189
  8. Shape theory for protein structure superposition

    1. Kanti V. Mardia, Vysaul B. Nyirongo
      Pages 209-230
  9. Graphical Models for Structure Prediction

    1. Front Matter
      Pages 231-231
  10. Graphical models for structure prediction

    1. Wouter Boomsma, Jes Frellsen, Thomas Hamelryck
      Pages 233-254
  11. Inferring Structure from Experimental Data

    1. Front Matter
      Pages 285-285
  12. Inferring structure from experimental data

About this book

Introduction

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.

Keywords

Bayesian statistics Bioinformatics Machine learning Protein structure prediction

Editors and affiliations

  • Thomas Hamelryck
    • 1
  • Kanti Mardia
    • 2
  • Jesper Ferkinghoff-Borg
    • 3
  1. 1., Department of BiologyUniversity of CopenhagenCopenhagenDenmark
  2. 2.School of Mathematics, Department of Statistics,University of LeedsLeedsUnited Kingdom
  3. 3.DTU Elektro, Department of Electrical EngineeringTechnical University of DenmarkKgs. LyngbyDenmark

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-27225-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-27224-0
  • Online ISBN 978-3-642-27225-7
  • Series Print ISSN 1431-8776
  • About this book