BNs - Bayesian Networks Applied to Modeling Cellular...

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Bayesian Networks Applied to Modeling Cellular Networks BMI/CS 576 www.biostat.wisc.edu/bmi576/ Mark Craven [email protected] Fall 2011 Bayesian networks a BN is a Directed Acyclic Graph (DAG) in which – the nodes denote random variables – each node X has a conditional probability distribution (CPD) representing P( X | Parents ( X ) ) the intuitive meaning of an arc from X to Y is that X directly influences Y formally: each variable X is independent of its non- descendants given its parents
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Probabilistic model of lac operon suppose we represent the system by the following discrete variables L (lactose) present, absent G (glucose) present, absent I (lacI) present, absent C (CAP) present, absent I-active (lacI unbound) present, absent C-active (CAP bound) present, absent Z (lacZ) high, low, absent suppose (realistically) the system is not completely deterministic the joint distribution of the variables could be specified by 2 6 ! 3 = 192 parameters A Bayesian network for the lac system Z L G C I I-active C-active
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This note was uploaded on 12/15/2011 for the course BMI 576 taught by Professor Staff during the Fall '11 term at Wisc Green Bay.

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BNs - Bayesian Networks Applied to Modeling Cellular...

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