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 more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g. neural networks, hidden Markov models, and belief networsk) are ideally suited for areas in which there is a lot of data but little theory. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible.In this book, the authors present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data.