Bioinformatics : the machine learning approach
Author(s)
Bibliographic Information
Bioinformatics : the machine learning approach
（Adaptive computation and machine learning）（Bradford book）
The MIT Press, 1998
- : hc
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Note
Includes bibliographical references(p. 319-346) and index
Description and Table of Contents
Description
The authors of this text present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students: firstly those biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function; and secondly those with a primary background in physics, mathematics, statistics or computer science who need to know more about specific applications in molecular biology.
Table of Contents
- Machine learning foundations - the probabilistic framework
- probabilistic modelling and inference - examples
- machine learning algorithms
- neural networks - the theory
- neural networks -applications
- hidden Markov models - the theory
- hidden Markov models - applications
- hybrid systems - hidden Markov Models and neural networks
- probabilistic models of evolution - phylogenetic trees
- stochastic grammars and linguistics
- Internet resources and public databases. Statistics
- information, theory, entropy, and relative entropy
- probabilistic graphical models
- HMM technicalities, scaling, periodic architectures, state functions, and Dirichlet mixtures
- list of main symbols and abbreviations.
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