The Complex Systems Reading Group will be meeting once again this Tuesday, March 16 at 7 p.m. at the Old Town Tavern (corner of Liberty and Ashley, in back under the large Rubenesque painting).
This week's topic is Bayesian networks, particularly their interesting applications to genetics, and we have a number of resources to peruse. Let me thank Ken Winter for his help in gathering them all.
First of all, for a very brief, simple description of the Bayesian networks and their potential, you should check out #4 in the
"10 Emerging Technologies That Will Change Your World" http://www.umich.edu/~warrencp/emergingtech.htm Gregory T Huang, Lauren Gravitz, Ivan Amato, Wade Roush, et. al. Technology Review 107(1):32 (2004).
The easiest way to find it is to search the document for "Bayesian".
--->Secondly, the primary paper for this week is a Science review article that looks at the application of Bayesian networks to genetics:
Inferring Cellular Networks Using Probabilistic Graphical Models http://www.umich.edu/~warrencp/799.pdf Nir Friedman Science 303(6):799-805 (2004)
High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.
Also, if you looking for further help with the concepts and details of Bayesian networks or if you are further interested in Bayesian networks, you may want to look at the helpful tutorials on David Heckerman's website:
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