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Network inference from dynamic (state) information
 

Summary: Network inference from dynamic (state)
information
Input: components; states of components (in time)
Hypotheses: regulatory framework
Output: proposed regulatory network
Validation: capture known interactionsValidation: capture known interactions
For inference of gene regulatory networks, the most frequently used state
information comes from gene expression arrays (microarrays)information comes from gene expression arrays (microarrays)
There are several microarray types and methods, for our purposes it
suffices to say that a microarray provides a readout of the relative orsuffices to say that a microarray provides a readout of the relative or
(semi)absolute expression level of each gene in the array.
Inference methods
ˇ Need expression snapshots:
­ Clustering analysis­ Clustering analysis
­ Bayesian networks
ˇ Need expression timecourse:
­ Continuous ­ Differential equationsContinuous Differential equations
­ Discrete - Boolean
ˇ Need other types of information:
­ Data miningg

  

Source: Albert, Réka - Departments of Biology & Physics, Pennsylvania State University

 

Collections: Biology and Medicine