Bioinformatics : the machine learning approach / Pierre Baldi, Sren Brunak.
By: Baldi, Pierre [author.]
.
Contributor(s): Brunak, Sren | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type: 
BookSeries: Adaptive computation and machine learning: Publisher: Cambridge, Massachusetts : MIT Press, c2001Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2001]Edition: 2nd ed.Description: 1 PDF (xxi, 452 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262255707.Subject(s): Bioinformatics | Molecular biology -- Computer simulation | Molecular biology -- Mathematical models | Neural networks (Computer science) | Machine learning | Markov processes | Computational Biology -- methods | Artificial Intelligence | Markov Chains | Models, Theoretical | Neural Networks (Computer)Genre/Form: Electronic books.Additional physical formats: Print version: No titleDDC classification: 572.8/01/13 Online resources: Abstract with links to resource Also available in print.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 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.
"A Bradford book."
Includes bibliographical references.
Restricted to subscribers or individual electronic text purchasers.
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.
Also available in print.
Mode of access: World Wide Web
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