Bayesian methods in structural bioinformatics
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 focus...
Kaydedildi:
| Yazar: | |
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| Diğer Yazarlar: | , |
| Materyal Türü: | Livre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2012.
Cham : Springer Nature |
| Seri Bilgileri: | Statistics for Biology and Health
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| Konular: | |
| Online Erişim: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Not: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Bayesian methods in structural bioinformatics, editors Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg, 2012, Berlin, Springer, 1 vol. (XXII-385 p.), Statistics for biology and health, 978-3-642-27224-0 • Bayesian Methods in Structural Bioinformatics, Texte imprimé, 9783642272264 • Bayesian Methods in Structural Bioinformatics, Texte imprimé, 9783642439889 |
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| 008 | 130325s2012 xx ||| |||| 00| 0 eng d | ||
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| 020 | |a 9783642272257 | ||
| 041 | 0 | |a eng | |
| 082 | |a 519.5 | ||
| 100 | 1 | |a Hamelryck, Thomas. | |
| 245 | 1 | 0 | |a Bayesian methods in structural bioinformatics |c edited by Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2012. | ||
| 490 | 0 | |a Statistics for Biology and Health |x 2197-5671 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a 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. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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. | ||
| 650 | |a Bioinformatique structurale |x Méthodes statistiques | ||
| 700 | 1 | |a Mardia, Kantilal Varichand, |d 1935- |4 pbd | |
| 700 | 1 | |a Ferkinghoff-Borg, Jesper. |4 pbd | |
| 776 | 0 | |0 161025323 |t Bayesian methods in structural bioinformatics |f editors Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg |d 2012 |c Berlin |n Springer |p 1 vol. (XXII-385 p.) |s Statistics for biology and health |z 978-3-642-27224-0 | |
| 776 | 0 | |t Bayesian Methods in Structural Bioinformatics |b Texte imprimé |z 9783642272264 | |
| 776 | 0 | |t Bayesian Methods in Structural Bioinformatics |b Texte imprimé |z 9783642439889 | |
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| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-VRJDNT4C-2 |z Accès sur la plateforme Istex | |
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