Bayesian Methods in Structural Bioinformatics
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 focus...
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| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg,
2012.
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| Series: | Statistics for Biology and Health,
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| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Part I Foundations: An Overview of Bayesian Inference and Graphical Models
- Monte Carlo Methods for Inferences in High-dimensional Systems
- Part II Energy Functions for Protein Structure Prediction: On the Physical Relevance and Statistical Interpretation of Knowledge based Potentials
- Statistical Machine Learning of Protein Energetics from Experimentally Observed Structures
- A Statistical View on the Reference Ratio Method
- Part III Directional Statistics and Shape Theory: Statistical Modelling and Simulation Using the Fisher-Bingham Distribution
- Statistics of Bivariate von Mises Distributions
- Bayesian Hierarchical Alignment Methods
- Likelihood and Empirical Bayes Superpositions of Multiple Macromolecular Structures
- Part IV Graphical models for structure prediction: Probabilistic Models of Local Biomolecular Structure and their Application in Structural Simulation
- Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields
- Part V Inferring Structure from Experimental Data
- Inferential Structure Determination from NMR Data
- Bayesian Methods in SAXS and SANS Structure Determination.
