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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. 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."

"This book is intended as a self-contained guide to the entire field of bioinformatics, interpreted as the application of information science to biology. There is a strong underlying belief that information is a profound concept underlying biology, and familiarity with the concepts of information should make it possible to gain many important new insights into biology. In other words, the vision underpinning this book goes beyond the narrow interpretation of bioinformatics sometimes encoun- tered, which may confine itself to specific tasks such as the attempted identification of genes in a DNA sequence."