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Hamelryck - Bayesian Methods in Structural Bioinformatics - 9783642272240 - V9783642272240
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Bayesian Methods in Structural Bioinformatics

€ 129.86
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Description for Bayesian Methods in Structural Bioinformatics Hardcover. This edited volume is a high-profile overview of the current state of play in statistical methods applied to structural bioinformatics. With almost 100 pages of introductory material, it covers topics including protein structure prediction and simulation. Editor(s): Hamelryck, Thomas; Mardia, Kanti V.; Ferkinghoff-Borg, Jesper. Series: Statistics for Biology and Health. Num Pages: 408 pages, 30 black & white tables, biography. BIC Classification: MBF; PBT; PHVN; PSA. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 25. Weight in Grams: 771.

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 ... Read more

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Product Details

Format
Hardback
Publication date
2012
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
400
Condition
New
Series
Statistics for Biology and Health
Number of Pages
386
Place of Publication
Berlin, Germany
ISBN
9783642272240
SKU
V9783642272240
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

About Hamelryck
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, ... Read more

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