# title
Detecting Multivariate Gene Interactions in RNA-Seq Data Using Optimal Bayesian Classification.

# abstract
Differential gene expression testing is an analysis commonly applied to RNA-Seq data. These statistical tests identify genes that are significantly different across phenotypes. We extend this testing paradigm to multivariate gene interactions from a classification perspective with the goal to detect novel gene interactions for the phenotypes of interest. This is achieved through our novel computational framework comprised of a hierarchical statistical model of the RNA-Seq processing pipeline and the corresponding optimal Bayesian classifier. Through Markov Chain Monte Carlo sampling and Monte Carlo integration, we compute quantities where no analytical formulation exists. The performance is then illustrated on an expression dataset from a dietary intervention study where we identify gene pairs that have low classification error yet were not identified as differentially expressed. Additionally, we have released the software package to perform OBC classification on RNA-Seq data under an open source license and is available at http://bit.ly/obc_package.

# publicationVenue.label
IEEE/ACM Transactions on Computational Biology and Bioinformatics

# authors[].organizations[].label
College of Agriculture and Life Sciences

# authors[].organizations[].label
College of Veterinary Medicine and Biomedical Sciences

# publisher.label
Institute of Electrical and Electronics Engineers (IEEE)

# authorList[]
Knight, J. M., Ivanov, I., Triff, K., Chapkin, R. S., & Dougherty, E. R.