Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

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$15.90
UPC:
9780262025065
Maximum Purchase:
3 units
Publisher:
A Bradford Book; second edition edition (August 1, 2001)
Shipping Weight:
2.2 pounds
Language:
English
ASIN:
026202506X
Series:
Adaptive Computation and Machine Learning series
Product Dimensions:
9.3 x 7.3 x 1.3 inches
Binding:
Hardcover
Pages:
476
ISBN-10:
9780262025065
ISBN-13:
978-0262025065
Author:
Sren Brunak,Pierre Baldi

Product Overview

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 modelsand to automate the process as much as possible.In this book Pierre Baldi and Sren 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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