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Bioinformatics - The Machine Learning Approach (Hardcover, second edition)
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Bioinformatics - The Machine Learning Approach (Hardcover, second edition)
Series: Bioinformatics
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A guide to machine learning approaches and their application to the
analysis of biological data. 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 Soren 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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