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Nederlands Buitenlands   Alles  Titel  Auteur  ISBN        
Levende natuur
Biologie algemeen
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Soren Brunak Pierre Baldi

Bioinformatics

€ 69.95



Taal / Language : English

Inhoudsopgave:
Series Foreword ix
Preface xi
Introduction
1(46)
Biological Data in Digital Symbol Sequences
1(6)
Genomes-Diversity, Size, and Structure
7(9)
Proteins and Proteomes
16(8)
On the Information Content of Biological Sequences
24(19)
Prediction of Molecular Function and Structure
43(4)
Machine-Learning Foundations: The Probabilistic Framework
47(20)
Introduction: Bayesian Modeling
47(3)
The Cox Jaynes Axioms
50(3)
Bayesian Inference and Induction
53(7)
Model Structures: Graphical Models and Other Tricks
60(4)
Summary
64(3)
Probabilistic Modeling and Inference: Examples
67(14)
The Simplest Sequence Models
67(6)
Statistical Mechanics
73(8)
Machine Learning Algorithms
81(18)
Introduction
81(1)
Dynamic Programming
82(1)
Gradient Descent
83(1)
EM/GEM Algorithms
84(3)
Markov-Chain Monte-Carlo Methods
87(4)
Simulated Annealing
91(2)
Evolutionary and Genetic Algorithms
93(1)
Learning Algorithms: Miscellaneous Aspects
94(5)
Neural Networks: The Theory
99(14)
Introduction
99(5)
Universal Approximation Properties
104(2)
Priors and Likelihoods
106(5)
Learning Algorithms: Backpropagation
111(2)
Neural Networks: Applications
113(52)
Sequence Encoding and Output Interpretation
114(5)
Sequence Correlations and Neural Networks
119(1)
Prediction of Protein Secondary Structure
120(13)
Prediction of Signal Peptides and Their Cleavage Sites
133(3)
Applications for DNA and RNA Nucleotide Sequences
136(17)
Prediction Performance Evaluation
153(2)
Different Performance Measures
155(10)
Hidden Markov Models: The Theory
165(24)
Introduction
165(5)
Prior Information and Initialization
170(2)
Likelihood and Basic Algorithms
172(5)
Learning Algorithms
177(7)
Applications of HMMs: General Aspects
184(5)
Hidden Markov Models: Applications
189(36)
Protein Applications
189(20)
DNA and RNA Applications
209(13)
Advantages and Limitations of HMMs
222(3)
Probabilistic Graphical Models in Bioinformatics
225(40)
The Zoo of Graphical Models in Bioinformatics
225(5)
Markov Models and DNA Symmetries
230(4)
Markov Models and Gene Finders
234(5)
Hybrid Models and Neural Network Parameterization of Graphical Models
239(2)
The Single-Model Case
241(14)
Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction
255(10)
Probabilistic Models of Evolution: Phylogenetic Trees
265(12)
Introduction to Probabilistic Models of Evolution
265(2)
Substitution Probabilities and Evolutionary Rates
267(2)
Rates of Evolution
269(1)
Data Likelihood
270(3)
Optimal Trees and Learning
273(1)
Parsimony
273(2)
Extensions
275(2)
Stochastic Grammars and Linguistics
277(22)
Introduction to Formal Grammars
277(1)
Formal Grammars and the Chomsky Hierarchy
278(6)
Applications of Grammars to Biological Sequences
284(4)
Prior Information and Initialization
288(1)
Likelihood
289(1)
Learning Algorithms
290(2)
Applications of SCFGs
292(1)
Experiments
293(2)
Future Directions
295(4)
Microarrays and Gene Expression
299(24)
Introduction to Microarray Data
299(2)
Probabilistic Modeling of Array Data
301(12)
Clustering
313(7)
Gene Regulation
320(3)
Internet Resources and Public Databases
323(24)
A Rapidly Changing Set of Resources
323(1)
Databases over Databases and Tools
324(1)
Databases over Databases in Molecular Biology
325(2)
Sequence and Structure Databases
327(6)
Sequence Similarity Searches
333(2)
Alignment
335(1)
Selected Prediction Servers
336(5)
Molecular Biology Software Links
341(2)
Ph.D. Courses over the Internet
343(1)
Bioinformatics Societies
344(1)
HMM/NN simulator
344(3)
A Statistics 347(10)
Decision Theory and Loss Functions
347(1)
Quadratic Loss Functions
348(1)
The Bias/Variance Trade-off
349(1)
Combining Estimators
350(1)
Error Bars
351(1)
Sufficient Statistics
352(1)
Exponential Family
352(1)
Additional Useful Distributions
353(1)
Variational Methods
354(3)
B Information Theory, Entropy, and Relative Entropy 357(8)
Entropy
357(2)
Relative Entropy
359(1)
Mutual Information
360(1)
Jensen`s Inequality
361(1)
Maximum Entropy
361(1)
Minimum Relative Entropy
362(3)
C Probabilistic Graphical Models 365(10)
Notation and Preliminaries
365(2)
The Undirected Case: Markov Random Fields
367(2)
The Directed Case: Bayesian Networks
369(6)
D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures 375(12)
Scaling
375(2)
Periodic Architectures
377(3)
State Functions: Bendability
380(2)
Dirichlet Mixtures
382(5)
E Gaussian Processes, Kernel Methods, and Support Vector Machines 387(12)
Gaussian Process Models
387(2)
Kernel Methods and Support Vector Machines
389(6)
Theorems for Gaussian Processes and SVMs
395(4)
F Symbols and Abbreviations 399(10)
References 409(38)
Index 447
Extra informatie: 
Hardback
476 pagina's
Januari 2001
1080 gram
236 x 182 x 32 mm
MIT Press Ltd us


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