000 03929nam a22005295i 4500
001 978-3-642-27225-7
003 DE-He213
005 20140220083307.0
007 cr nn 008mamaa
008 120322s2012 gw | s |||| 0|eng d
020 _a9783642272257
_9978-3-642-27225-7
024 7 _a10.1007/978-3-642-27225-7
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMBNS
_2bicssc
072 7 _aMED090000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aHamelryck, Thomas.
_eeditor.
245 1 0 _aBayesian Methods in Structural Bioinformatics
_h[electronic resource] /
_cedited by Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2012.
300 _aXXII, 385p. 86 illus., 7 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Biology and Health,
_x1431-8776
505 0 _aPart 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.
520 _aThis 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.
650 0 _aStatistics.
650 0 _aMedicine.
650 0 _aBioinformatics.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
650 2 4 _aMolecular Medicine.
650 2 4 _aBiophysics and Biological Physics.
650 2 4 _aMathematical and Computational Biology.
650 2 4 _aComputational Biology/Bioinformatics.
700 1 _aMardia, Kanti.
_eeditor.
700 1 _aFerkinghoff-Borg, Jesper.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642272240
830 0 _aStatistics for Biology and Health,
_x1431-8776
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-27225-7
912 _aZDB-2-SMA
999 _c102539
_d102539