Return to home page
Searching: Muskingum library catalog
We are currently experiencing delivery delays for items requested from other institutions while transitioning to a new statewide delivery service. Please contact your library with questions or advice about alternative resources. Thank you for your patience!
  Previous Record Previous Item Next Item Next Record
  Reviews, Summaries, etc...
EBOOK
Title Bayesian methods in structural bioinformatics / Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg, editors.
Imprint Berlin ; New York : Springer, 2012.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Statistics for biology and health, 1431-8776
Statistics for biology and health.
Subject Structural bioinformatics -- Statistical methods.
Computational Biology -- statistics & numerical data.
Alt Name Hamelryck, Thomas.
Mardia, K. V.
Ferkinghoff-Borg, Jesper.
Description 1 online resource (xxii, 385 pages) : portraits.
Bibliography Note Includes bibliographical references and index.
Contents Part 1. Foundations -- An Overview of Bayesian Inference and Graphical Models / Thomas Hamelryck -- Monte Carlo Methods for Inference in High-Dimensional Systems / Jesper Ferkinghoff-Borg -- Part 2. Energy Functions for Protein Structure Prediction -- On the Physical Relevance and Statistical Interpretation of Knowledge-Based Potentials / Mikael Borg, Thomas Hamelryck and Jesper Ferkinghoff-Borg -- Towards a General Probabilistic Model of Protein Structure: The Reference Ratio Method / Jes Frellsen, Kanti V. Mardia, Mikael Borg, Jesper Ferkinghoff-Borg and Thomas Hamelryck -- Inferring Knowledge Based Potentials Using Contrastive Divergence / Alexei A. Podtelezhnikov and David L. Wild -- Part 3. Directional statistics for biomolecular structure -- Statistics of Bivariate von Mises Distributions / Kanti V. Mardia and Jes Frellsen -- Statistical Modelling and Simulation Using the Fisher-Bingham Distribution / John T. Kent -- Part 4. Shape Theory for Protein Structure Superposition -- Likelihood and Empirical Bayes Superposition of Multiple Macromolecular Structures / Douglas L. Theobald -- Bayesian Hierarchical Alignment Methods / Kanti V. Mardia and Vysaul B. Nyirongo -- Part 5. Graphical models for structure prediction -- Probabilistic Models of Local Biomolecular Structure and Their Applications / Wouter Boomsma, Jes Frellsen and Thomas Hamelryck -- Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields / Chen Yanover and Menachem Fromer -- Part 6. Inferring Structure from Experimental Data -- Inferential Structure Determination from NMR Data / Michael Habeck -- Bayesian Methods in SAXS and SANS Structure Determination / Steen Hansen.
Summary 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.
Note English.
ISBN 9783642272257 (electronic bk.)
3642272258 (electronic bk.)
364227224X (Cloth)
9783642272240 (Cloth)
9783642272240
ISBN/ISSN 9786613711366
OCLC # 783219562
Additional Format Print version: Bayesian methods in structural bioinformatics. Berlin ; New York : Springer, 2012 (DLC) 2012933773



If you experience difficulty accessing or navigating this content, please contact the OPAL Support Team