NDLI: Relative expression analysis for identifying perturbed pathways

Content Provider	IEEE Xplore Digital Library
Author	Eddy, J.A. Geman, D. Price, N.D.
Copyright Year	2009
Description	Author affiliation: Department of Bioengineering, University of Illinois, Urbana, IL 61801 USA (Eddy, J.A.) \|\| Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218 USA (Geman, D.) \|\| Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, IL 61801 USA (Price, N.D.)
Abstract	The computational identification from global data sets of stable and predictive patterns of gene and protein relative expression reversals offers a simple, yet powerful approach to target therapies for personalized medicine and to identify pathways that are disease-perturbed. We previously utilized this approach to identify a molecular classifier with near 100% accuracy for differentiating gastrointestinal stromal tumor (GIST) and leiomyosarcoma (LMS), two cancers that have very similar histopathology, but require very different treatments. Differential Rank Conservation (DIRAC) is a novel approach for studying gene ordering within pathways and is based on the relative expression ranks of participating genes. DIRAC provides quantitative measures of how pathway rankings differ both within and between phenotypes. DIRAC between pathways in a selected phenotype contrasts the scenarios where either (i) pathways are ranked similarly in all samples; or (ii) the ordering of pathway genes is highly varied. We examined gene expression in GIST and LMS tumor profiles and identified pathways that appear to be tightly regulated based on high conservation of gene ordering. The second form of DIRAC manifests as a change in ranking (i.e., shuffling) between phenotypes for a selected pathway. These variably expressed pathways serve as signatures for molecular classification, and the ability to accurately classify microarray samples provided strong validation for the pathway-level expression differences identified by DIRAC.
Starting Page	5456
Ending Page	5459
File Size	742916
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5334063
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cancer Gene expression Neoplasms Least squares approximation Gastrointestinal tract Diseases USA Councils Robustness Displays Intestines
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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