Translation of disease associated gene signatures across tissues. (1st January 2015)

• Record Type: Journal Article
• Authors: Kasim, Adetayo; Shkedy, Ziv; Lin, Dan; Sanden, Suzy Van; Abrahantes, Josè Cortiñas; Göhlmann, Hinrich W.H.; Bijnens, Luc; Yekutieli, Dani; Camilleri, Michael; Aerssens, Jeroen; Talloen, Willem
• Is Part Of: International journal of data mining and bioinformatics. Volume 11:Number 3(2015)
• Page Start: 301
• Page End: 313
• Publication Date: 2015-01-01
• Subjects: gene expression -- joint modelling -- surrogate markers -- biomarkers -- target tissues -- surrogate tissue -- feature selection -- classification -- class prediction -- irritable bowel syndrome -- IBS -- disease associated gene signatures -- ANOVA -- colon tissue -- rectum tissue -- bioinformatics; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• DOI: 10.1504/IJDMB.2015.067321
• Languages: English
• ISSNs: 1748-5673

Abstract

It has recently been shown that disease associated gene signatures can be identified by profiling tissue other than the disease related tissue. In this paper, we investigate gene signatures for Irritable Bowel Syndrome (IBS) using gene expression profiling of both disease related tissue (colon) and surrogate tissue (rectum). Gene specific joint ANOVA models were used to investigate differentially expressed genes between the IBS patients and the healthy controls taken into account both intra and inter tissue dependencies among expression levels of the same gene. Classification algorithms in combination with feature selection methods were used to investigate the predictive power of gene expression levels from the surrogate and the target tissues. We conclude based on the analyses that expression profiles of the colon and the rectum tissue could result in better predictive accuracy if the disease associated genes are known.