Baldi
Bioinformatics: The Machine Learning Approach.
Machine learning approaches play a significant role in bioinformatics due to the abundance of highly variable data and the lack of comprehensive theories. This tutorial provides a broad overview of machine learning approaches in bioinformatics. Specifically, the main topics are:
1. The Bayesian probabilistic framework for modeling and induction as the common foundation for all machine learning algorithms. 2. A brief presentation of a number of important classes of models routinely used in bioinformatics applications such as: neural networks, hidden Markov models, stochastic grammars, and belief networks and the corresponding learning algorithms. 3. Examples of specific applications, such as: -neural networks for protein functional sites and secondary structure prediction; -hidden Markov models for data base searches, multiple alignments, and pattern discovery; -hidden Markov models for gene finding and promoter prediction; -stochastic grammars and RNA modeling;Reference: P. Baldi and S. Brunak (1998) "Bioinformatics: the Machine Learning Approach" MIT Press.