Translation of disease associated gene signatures across tissues

Adetayo Kasim, Ziv Shkedy, Dan Lin, Suzy Van Sanden, Josè Cortiñas Abrahantes, Hinrich W.H. Göhlmann, Luc Bijnens, Dani Yekutieli, Michael Camilleri, Jeroen Aerssens, Willem Talloen

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)301-313
Number of pages13
JournalInternational Journal of Data Mining and Bioinformatics
Volume11
Issue number3
DOIs
StatePublished - 2015

Keywords

  • Biomarker
  • Classification and class prediction and irritable bowel syndrome
  • Features selection
  • Gene expression
  • Joint modelling
  • Surrogate markers
  • Surrogate tissue
  • Target tissue

ASJC Scopus subject areas

  • Library and Information Sciences
  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

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  • Cite this

    Kasim, A., Shkedy, Z., Lin, D., Van Sanden, S., Abrahantes, J. C., Göhlmann, H. W. H., Bijnens, L., Yekutieli, D., Camilleri, M., Aerssens, J., & Talloen, W. (2015). Translation of disease associated gene signatures across tissues. International Journal of Data Mining and Bioinformatics, 11(3), 301-313. https://doi.org/10.1504/IJDMB.2015.067321