lecture_13

lecture_13 - 7.90J 6.874J Computational functional genomics...

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7.90J 6.874J Computational functional genomics (Spring 2005: Lecture 13) David K. Gifford (Adapted from a lecture by Tommi S. Jaakkola) MIT CSAIL
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Topics Modeling Biological Systems A simple biological system Model assumptions Discrete Bayesian networks Discretizing data Bayesian scoring functions Edge scores
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A simple biological system Data are observed from a biological subnetwork with four genes The genes might influence one anothers expression The structure of the network is hidden from us (we do not see the edges) We observe mRNA and active protein levels for each of the four genes We have hundreds of observations
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Model assumptions There are no hidden variables (only A, B, C, and D can influence one another) There are no cycles in the unknown network ”Sufficient conditions” are observed to perturb the expession of A, B, C, and D All observed data are continuous Data is complete (no missing variable observations) The underlying biological system can be modeled using discrete states Uniform population behavior (Why is this important?) We begin with 8 nodes. ..
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networks Nodes represent variables Each node has 0 or more parents The structure S of edges describes how the joint probability dis- tribution of the observed variables can be factored S encodes the conditional independence of the observed variables To fully specify a network we need to specify how children depend on their parents This dependency is encoded in the parameters θ Given n variables, roughly how many structures are there? 2
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lecture_13 - 7.90J 6.874J Computational functional genomics...

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