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Adaptive Computation and Machine Learning
Bioinformatics: The Machine Learning Approach
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.
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.
476 pages, Hardcover
First published February 13, 1998
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About the author
Pierre Baldi
6 books1 followerPierre 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.
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Displaying 1 - 3 of 3 reviews
July 11, 2021
A bit biased since I did my master's thesis at Center for Biological Sequence Analysis, the lab headed by one of the authors. That said, this is a fantastic book. Focus is on sequence (DNA, RNA, protein) bioinformatics and a must-read if you are active in that field. I would also go so far as to say that it is a great book on bayesian methods and machine learning in general and can be read by someone not active in bioinformatics. Highly recommended.
September 8, 2007
It's good, my advisor wrote it.
April 10, 2009
Read chapter 5 (neural networks) and skimmed 6 (applications of neural networks); good theoretical foundation.
Displaying 1 - 3 of 3 reviews