| Bioinformatics, Second Edition: The Machine Learning Approach Contributor(s): Baldi, Pierre (Author), Brunak, Søren (Author) |
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ISBN: 026202506X ISBN-13: 9780262025065 Publisher: Bradford Book OUR PRICE: $69.30 Product Type: Hardcover Published: July 2001 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Computers | Neural Networks - Science | Life Sciences - Biochemistry |
| Dewey: 572.801 |
| LCCN: 2001030210 |
| Series: Adaptive Computation and Machine Learning |
| Physical Information: 1.28" H x 7.33" W x 9.34" (2.38 lbs) 476 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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 S ren 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. |
Contributor Bio(s): Baldi, Pierre: - Pierre Baldi is Professor of Information and Computer Science and of Biological Chemistry (College of Medicine) and Director of the Institute for Genomics and Bioinformatics at the University of California, Irvine.Brunak, Soren: - Søren Brunak is Professor and Director of the Center for Biological Sequence Analysis at the Technical University of Denmark. |