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  • Book
  • © 2012

Bayesian Methods in Structural Bioinformatics

  • 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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Table of contents (13 chapters)

  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. On the Physical Relevance and Statistical Interpretation of Knowledge-Based Potentials

      • Mikael Borg, Thomas Hamelryck, Jesper Ferkinghoff-Borg
      Pages 97-124
    2. Towards a General Probabilistic Model of Protein Structure: The Reference Ratio Method

      • Jes Frellsen, Kanti V. Mardia, Mikael Borg, Jesper Ferkinghoff-Borg, Thomas Hamelryck
      Pages 125-134
    3. Inferring Knowledge Based Potentials Using Contrastive Divergence

      • 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

    1. Statistics of Bivariate von Mises Distributions

      • Kanti V. Mardia, Jes Frellsen
      Pages 159-178
  7. Shape Theory for Protein Structure Superposition

    1. Front Matter

      Pages 189-189
  8. Shape theory for protein structure superposition

    1. Bayesian Hierarchical Alignment Methods

      • 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. Probabilistic Models of Local Biomolecular Structure and Their Applications

      • 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

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

  • , Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Thomas Hamelryck

  • School of Mathematics, Department of Statistics,, University of Leeds, Leeds, United Kingdom

    Kanti Mardia

  • DTU Elektro, Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark

    Jesper Ferkinghoff-Borg

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. 

Bibliographic Information

Buying options

eBook EUR 85.59
Price includes VAT (Finland)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book EUR 109.99
Price includes VAT (Finland)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book EUR 109.99
Price includes VAT (Finland)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions