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...
שמור ב:
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| מחברים אחרים: | , |
| פורמט: | Livre numérique |
| שפה: | Anglais |
| יצא לאור: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2012.
Cham : Springer Nature |
| סדרה: | Statistics for Biology and Health
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| נושאים: | |
| גישה מקוונת: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| הערה: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Bayesian methods in structural bioinformatics, editors Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg, 2012, Berlin, Springer, 1 vol. (XXII-385 p.), Statistics for biology and health, 978-3-642-27224-0 • Bayesian Methods in Structural Bioinformatics, Texte imprimé, 9783642272264 • Bayesian Methods in Structural Bioinformatics, Texte imprimé, 9783642439889 |
תוכן הענינים:
- 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.