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

    1. Front Matter
      Pages 157-157
    2. Kanti V. Mardia, Jes Frellsen
      Pages 159-178
  5. Shape Theory for Protein Structure Superposition

    1. Front Matter
      Pages 189-189
    2. Kanti V. Mardia, Vysaul B. Nyirongo
      Pages 209-230
  6. Graphical Models for Structure Prediction

    1. Front Matter
      Pages 231-231
    2. Wouter Boomsma, Jes Frellsen, Thomas Hamelryck
      Pages 233-254
  7. Inferring Structure from Experimental Data

    1. Front Matter
      Pages 285-285
  8. Back Matter
    Pages 343-385

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.


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