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. 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 in which there is a lot of data but little theory, as in molecular biology. The goal in machine learning is to extract useful information from a body by building good probabilistic models - and to automate the process as much as possible.
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. This 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 edition contains expanded coverage of probabilistic graphical models and the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.