Bioinformatics: The Machine Learning Approach

Pierre Baldi et Soren Brunak

ISBN 10: 026202442X / ISBN 13: 9780262024426

Published by MIT Press, 1998

About this Item

Ancien livre de bibliothèque. Traces d'usure sur la couverture. Couverture différente. Edition 1998. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Signs of wear on the cover. Different cover. Edition 1998. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Bibliographic Details

• Title: Bioinformatics: The Machine Learning Approach
• Publisher: MIT Press
• Publication Date: 1998
• Language: English
• ISBN 10: 026202442X
• ISBN 13: 9780262024426
• Binding: Hardcover
• Condition: Bon

About this title

Synopsis

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 more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science.

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--and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is 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 at two types of researchers and students. First are the 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. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

About the Author

Pierre Baldi is Chairman of the Board, Net-ID, Inc. Søren Brunak is Director, Center for Biological Sequence Analysis, The Technical University of Denmark.