A Bayesian Approach to Inferring Gene Activity States from Gene Expression Data
Presented by:
Craig Disselkoen
Abstract:
A generally accepted model of gene activity suggests that most genes are
either in an active or inactive state at any time/in any given condition.
Downstream analyses of gene expression data are highly dependent upon the ability
to correctly classify a given gene as active or inactive. Current state of the art
methods for determining gene activity state based on observed measures of gene
expression make limiting assumptions and do not capture statistical uncertainty.
We will present and discuss innovative new approaches which address limitations in
current methods.