Bioinformatics the machine learning approach
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and appl...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Cambridge, Massachusetts
The MIT Press
2001
MIT Press A Bradford Book |
| Edition | 2nd edition. |
| Series | Adaptive computation and machine learning |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780262255707 0262255707 026202506X 9780262025065 |
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| Summary: | An unprecedented wealth of data is being generated by genome sequencing
projects and other experimental efforts to determine the structure and function of
biological molecules. The demands and opportunities for interpreting these data are
expanding rapidly. Bioinformatics is the development and application of computer
methods for management, analysis, interpretation, and prediction, as well as for the
design of experiments. Machine learning approaches (e.g., neural networks, hidden
Markov models, and belief networks) are ideally suited for areas where there is a
lot of data but little theory, which is the situation in molecular biology. The goal
in machine learning is to extract useful information from a body of data by building
good probabilistic models -- and to automate the process as much as
possible.In this book Pierre Baldi and Søren Brunak present the
key machine learning approaches and apply them to the computational problems
encountered in the analysis of biological data. The book is aimed both at biologists
and biochemists who need to understand new data-driven algorithms and at those with
a primary background in physics, mathematics, statistics, or computer science who
need to know more about applications in molecular biology.This
new second edition contains expanded coverage of probabilistic graphical models and
of the applications of neural networks, as well as a new chapter on microarrays and
gene expression. The entire text has been extensively revised. |
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| Bibliography: | A Bradford book. Includes bibliographical references (p. 409-445) and index |
| ISBN: | 9780262255707 0262255707 026202506X 9780262025065 |