Bayesian methods in structural bioinformatics:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | German |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2012
|
| Schriftenreihe: | Statistics for biology and health
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XXII, 385 S. Ill., graph. Darst. |
| ISBN: | 9783642272240 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804149079436951552 |
|---|---|
| adam_text | Contents
Part I Foundations
1
An Overview of Bayesian Inference and Graphical Models
........... 3
Thomas Hamelryck
2
Monte Carlo Methods for Inference in High-Dimensional
Systems
...................................................................... 49
Jesper Ferkinghoff-Borg
Part II Energy Functions for Protein Structure Prediction
3
On the Physical Relevance and Statistical Interpretation
of Knowledge-Based Potentials
........................................... 97
Mikael
Borg,
Thomas Hamelryck, and
Jesper
Ferkinghoff-
Borg
4
Towards a General Probabilistic Model of Protein
Structure: The Reference Ratio Method
................................. 125
Jes
Freiisen,
Kanti V.
Mardia,
Mikael Borg, Jesper
Ferkinghoff-Borg,
and Thomas Hamelryck
5
Inferring Knowledge Based Potentials Using
Contrastive
Divergence
................................................................... 135
Alexei A. Podtelezhnikov and David L. Wild
Part HI Directional Statistics for Biomolecular Structure
6
Statistics of Bivariate
von
Mises
Distributions
.......................... 159
Kanti V.
Mardia and
Jes
Freiisen
7
Statistical Modelling and Simulation Using
the Fisher-Bingham Distribution
......................................... 179
John T. Kent
xvn
xviii Contents
Part IV Shape Theory for Protein Structure Superposition
8
Likelihood and Empirical
Bayes
Superposition of Multiple
Macromolecular Structures
............................................... 191
Douglas L. Theobald
9
Bayesian Hierarchical Alignment Methods
.............................. 209
Kanti
V.
Mardia
and Vysaul B. Nyirongo
Part V Graphical Models for Structure Prediction
10
Probabilistic Models of Local Biomolecular Structure and
Their Applications
.......................................................... 233
Wouter Boomsma,
Jes Frellsen,
and Thomas Hamelryck
11
Prediction of Low Energy Protein Side Chain
Configurations Using Markov Random Fields
.......................... 255
Chen Yanover and Menachem Fromer
Part VI Inferring Structure from Experimental Data
12
Inferential Structure Determination from NMR Data
................. 287
Michael Habeck
13
Bayesian Methods in SAXS and SANS Structure Determination
..... 313
Steen
Hansen
References
......................................................................... 343
Index
............................................................................... 377
Statistics for Biology and Health
Thomas Hamelryck
·
Kanti Mardia
■
Jesper Ferkinghoff-Borg
Editors
Bayesian Methods in
Structurai 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 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.
Stochastic models and statistical inference have heralded a revolution in sequence and
genome analysis, yielding great benefits for research and medicine. Now structural
analysis is following suit. This book will be extremely useful for any interested in
modern structural biology and using the present flood of structural data optimally. It
both covers necessary theory and exciting applications beautifully.
Jotun
Hein,
Professor of Bioinformatics, University of Oxford
This book provides the first comprehensive overview of the use of Bayesian methods
in structural biology. By covering the subject both broadly and in depth, this book is a
must-have and will be invaluable both to experts as well as to students entering the field.
Kresten Lindorff-Larsen, Associate professor, University of Copenhagen
|
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| discipline | Biologie Mathematik |
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| spelling | Bayesian methods in structural bioinformatics Thomas Hamelryck ..., ed. Berlin [u.a.] Springer 2012 XXII, 385 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistics for biology and health Proteine (DE-588)4076388-2 gnd rswk-swf Molekülstruktur (DE-588)4170383-2 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bayes-Inferenz (DE-588)4648118-7 gnd rswk-swf Struktur (DE-588)4058125-1 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Bayes-Verfahren (DE-588)4204326-8 s Bioinformatik (DE-588)4611085-9 s Proteine (DE-588)4076388-2 s Struktur (DE-588)4058125-1 s DE-604 Molekülstruktur (DE-588)4170383-2 s Bayes-Inferenz (DE-588)4648118-7 s 2\p DE-604 Hamelryck, Thomas edt Erscheint auch als Online-Ausgabe 978-3-642-27225-7 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024976324&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024976324&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bayesian methods in structural bioinformatics Proteine (DE-588)4076388-2 gnd Molekülstruktur (DE-588)4170383-2 gnd Bioinformatik (DE-588)4611085-9 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Inferenz (DE-588)4648118-7 gnd Struktur (DE-588)4058125-1 gnd |
| subject_GND | (DE-588)4076388-2 (DE-588)4170383-2 (DE-588)4611085-9 (DE-588)4204326-8 (DE-588)4648118-7 (DE-588)4058125-1 (DE-588)4143413-4 |
| title | Bayesian methods in structural bioinformatics |
| title_auth | Bayesian methods in structural bioinformatics |
| title_exact_search | Bayesian methods in structural bioinformatics |
| title_full | Bayesian methods in structural bioinformatics Thomas Hamelryck ..., ed. |
| title_fullStr | Bayesian methods in structural bioinformatics Thomas Hamelryck ..., ed. |
| title_full_unstemmed | Bayesian methods in structural bioinformatics Thomas Hamelryck ..., ed. |
| title_short | Bayesian methods in structural bioinformatics |
| title_sort | bayesian methods in structural bioinformatics |
| topic | Proteine (DE-588)4076388-2 gnd Molekülstruktur (DE-588)4170383-2 gnd Bioinformatik (DE-588)4611085-9 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Inferenz (DE-588)4648118-7 gnd Struktur (DE-588)4058125-1 gnd |
| topic_facet | Proteine Molekülstruktur Bioinformatik Bayes-Verfahren Bayes-Inferenz Struktur Aufsatzsammlung |
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