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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.
Table des matières
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.
A propos de l'auteur
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.
Résumé
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.
