Bioinformatics: The Machine Learning Approach

Abstract

The first book in the new series on Adaptive Computation and Machine Learn- ing, Pierre Baldi and Søren Brunak’s Bioinformatics provides a comprehensive introduction to the application of machine learning in bioinformatics. The development of techniques for sequencing entire genomes is providing astro- nomical amounts of DNA and protein sequence data that have the potential to revolutionize biology. To analyze this data, new computational tools are needed—tools that apply machine learning algorithms to fit complex stochas- tic models. Baldi and Brunak provide a clear and unified treatment of statisti- cal and neural network models for biological sequence data. Students and re- searchers in the fields of biology and computer science will find this a valuable and accessible introduction to these powerful new computational techniques. The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, in- cluding computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientific and industrial fields. Recently, several research communities have begun to converge on a common set of issues surrounding supervised, unsupervised, and reinforce- ment learning problems. The MIT Press series on Adaptive Computation and Machine Learning seeks to unify themany diverse strands ofmachine learning research and to foster high quality research and innovative applications.