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Bioinformatics : the machine learning approach
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Bioinformatics : the machine learning approach

Author: Pierre Baldi; Søren Brunak
Publisher: Cambridge, Mass. : MIT Press, ©1998.
Series: Adaptive computation and machine learning.
Edition/Format:   eBook : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Baldi, Pierre.
Bioinformatics.
Cambridge, Mass. : MIT Press, c1998
(DLC) 97036102
(OCoLC)37437670
Material Type: Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Pierre Baldi; Søren Brunak
ISBN: 0585038023 9780585038025
OCLC Number: 43474595
Notes: "A Bradford book."
Description: 1 online resource (xviii, 351 p.) : ill. (some col.)
Contents: Biological Data in Digital Symbol Sequences --
Genomes--Diversity, Size, and Structure --
Proteins and Proteomes --
On the Information Content of Biological Sequences --
Prediction of Molecular Function and Structure --
Machine Learning Foundations: The Probabilistic Framework --
Introduction: Bayesian Modeling --
The Cox-Jaynes Axioms --
Bayesian Inference and Induction --
Model Structures: Graphical Models and Other Tricks --
Probabilistic Modeling and Inference: Examples --
The Simplest Sequence Models --
Statistical Mechanics --
Machine Learning Algorithms --
Dynamic Programming --
Gradient Descent --
EM/GEM Algorithms --
Markov Chain Monte Carlo Methods --
Simulated Annealing --
Evolutionary and Genetic Algorithms --
Learning Algorithms: Miscellaneous Aspects --
Neural Networks: The Theory --
Universal Approximation Properties --
Priors and Likelihoods --
Learning Algorithms: Backpropagation --
Neural Networks: Applications --
Sequence Encoding and Output Interpretation --
Prediction of Protein Secondary Structure --
Prediction of Signal Peptides and Their Cleavage Sites --
Applications for DNA and RNA Nucleotide Sequences --
Hidden Markov Models: The Theory --
Prior Information and Initialization --
Likelihood and Basic Algorithms --
Learning Algorithms --
Applications of HMMs: General Aspects --
Hidden Markov Models: Applications --
Protein Applications --
DNA and RNA Applications --
Conclusion: Advantages and Limitations of HMMs --
Hybrid Systems: Hidden Markov Models and Neural Networks --
Introduction to Hybrid Models.
Series Title: Adaptive computation and machine learning.
Responsibility: Pierre Baldi, Søren Brunak.

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