With:

Davina Durgana, Walk Free Foundation
Tim Hesterberg, Google
Stacy Winham, Mayo Clinic

With:

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

Presented by:

Wenjun Kong, Rose-Hulman Institute of Technology

Abstract

Abstract

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.