Davina Durgana, Walk Free Foundation
Kurt Cogswell, Prof and Chair of the department of Mathematics and Statistics, South Dakota State University
Lance Waller, Prof and Chair of the department of Biostatistics and Bioinformatics, Emory University
Wenjun Kong, Rose-Hulman Institute of Technology
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.