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 Søren 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.

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) PDF

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