Bioinformatics: The Machine Learning Approach

Adaptive Computation and Machine Learning

by Pierre Baldi, Søren Brunak

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 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.

Genres: Biology, Reference, Nonfiction

477 pages, Unknown Binding

First published February 13, 1998

About the author

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.

Reviews

[Carl · Rating 5 out of 5]

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.

[Kenny Daily · Rating 4 out of 5]

It's good, my advisor wrote it.

[David Robins · Rating 4 out of 5]

Read chapter 5 (neural networks) and skimmed 6 (applications of neural networks); good theoretical foundation.