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Logo of bmcgenoBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Genomics
 
BMC Genomics. 2009; 10: 583.
Published online Dec 5, 2009. doi:  10.1186/1471-2164-10-583
PMCID: PMC2797819
Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases
Lucas B Edelman,1,2,6 Giuseppe Toia,1,2 Donald Geman,4 Wei Zhang,5 and Nathan D Pricecorresponding author1,2,3
1Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
2Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
3Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
4Department of Applied Mathematics and Statistics & Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21218, USA
5Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
6Babraham Institute, Cambridge, CB22 3AT, UK
corresponding authorCorresponding author.
Lucas B Edelman: le253/at/cam.ac.uk; Giuseppe Toia: gtoia2/at/illinois.edu; Donald Geman: geman/at/cis.jhu.edu; Wei Zhang: wzhang/at/mdanderson.org; Nathan D Price: ndprice/at/illinois.edu
Received May 13, 2009; Accepted December 5, 2009.
Abstract
Background
Identification of molecular classifiers from genome-wide gene expression analysis is an important practice for the investigation of biological systems in the post-genomic era - and one with great potential for near-term clinical impact. The 'Top-Scoring Pair' (TSP) classification method identifies pairs of genes whose relative expression correlates strongly with phenotype. In this study, we sought to assess the effectiveness of the TSP approach in the identification of diagnostic classifiers for a number of human diseases including bacterial and viral infection, cardiomyopathy, diabetes, Crohn's disease, and transformed ulcerative colitis. We examined transcriptional profiles from both solid tissues and blood-borne leukocytes.
Results
The algorithm identified multiple predictive gene pairs for each phenotype, with cross-validation accuracy ranging from 70 to nearly 100 percent, and high sensitivity and specificity observed in most classification tasks. Performance compared favourably with that of pre-existing transcription-based classifiers, and in some cases was comparable to the accuracy of current clinical diagnostic procedures. Several diseases of solid tissues could be reliably diagnosed through classifiers based on the blood-borne leukocyte transcriptome. The TSP classifier thus represents a simple yet robust method to differentiate between diverse phenotypic states based on gene expression profiles.
Conclusion
Two-transcript classifiers have the potential to reliably classify diverse human diseases, through analysis of both local diseased tissue and the immunological response assayed through blood-borne leukocytes. The experimental simplicity of this method results in measurements that can be easily translated to clinical practice.
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