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| Título : |
Bayesian Methods in Structural Bioinformatics |
| Tipo de documento: |
documento electrónico |
| Autores: |
SpringerLink (Online service) ; Hamelryck, Thomas ; Mardia, Kanti ; Ferkinghoff-Borg, Jesper |
| Editorial: |
Berlin, Heidelberg : Springer Berlin Heidelberg |
| Fecha de publicación: |
2012 |
| Colección: |
Statistics for Biology and Health, ISSN 1431-8776 |
| Número de páginas: |
XXII, 386 p |
| Il.: |
online resource |
| ISBN/ISSN/DL: |
978-3-642-27225-7 |
| Idioma : |
Inglés (eng) |
| Palabras clave: |
Statistics Molecular biology Bioinformatics Biomathematics Biophysics Biological physics for Life Sciences, Medicine, Health Sciences Medicine and Physics Mathematical Computational Biology Biology/Bioinformatics |
| Clasificación: |
51 Matemáticas |
| Resumen: |
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 |
| Nota de contenido: |
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 |
| En línea: |
http://dx.doi.org/10.1007/978-3-642-27225-7 |
| Link: |
https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32965 |
Bayesian Methods in Structural Bioinformatics [documento electrónico] / SpringerLink (Online service) ; Hamelryck, Thomas ; Mardia, Kanti ; Ferkinghoff-Borg, Jesper . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2012 . - XXII, 386 p : online resource. - ( Statistics for Biology and Health, ISSN 1431-8776) . ISBN : 978-3-642-27225-7 Idioma : Inglés ( eng)
| Palabras clave: |
Statistics Molecular biology Bioinformatics Biomathematics Biophysics Biological physics for Life Sciences, Medicine, Health Sciences Medicine and Physics Mathematical Computational Biology Biology/Bioinformatics |
| Clasificación: |
51 Matemáticas |
| Resumen: |
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 |
| Nota de contenido: |
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 |
| En línea: |
http://dx.doi.org/10.1007/978-3-642-27225-7 |
| Link: |
https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32965 |
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