Tutorial: Bayesian Networks for Computational Biology

Needham et al. have come up with a nice "Primer on Learning in Bayesian Networks for Computational Biology" [pdf: http://compbiol.plosjournals.org/perlserv/?request=get-pdf&file=10.1371_journal.pcbi.0030129-L.pdf ].
If you have matlab, then you can start with "Bayes Net Toolbox for Matlab" [ http://bnt.sourceforge.net/ ] or of you are R fan, then you can read http://www.springer.com/west/home?SGWID=4-102-22-173732008-0&changeHeader=true&SHORTCUT=www.springer.com/978-0-387-71384-7 and get going.
All the best

clipped from compbiol.plosjournals.org Bayesian networks (BNs) provide a neat and compact representation for expressing joint probability distributions (JPDs) and for inference. They are becoming increasingly important in the biological sciences for the tasks of inferring cellular networks [1], modelling protein signalling pathways [2], systems biology, data integration [3], classification [4], and genetic data analysis [5]. The representation and use of probability theory makes BNs suitable for combining domain knowledge and data, expressing causal relationships, avoiding overfitting a model to training data, and learning from incomplete datasets. The probabilistic formalism provides a natural treatment for the stochastic nature of biological systems and measurements. This primer aims to introduce BNs to the computational biologist, focusing on the concepts behind methods for learning the parameters and structure of models, at a time when they are becoming the machine learning method of choice.