Research article
The use of genetic programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer
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* Corresponding author: Ram H Datar datar@usc.edu
- † Equal contributors
1 Department of Pathology, University of Southern California Keck School of Medicine, 2011 Zonal Avenue, HMR 312, Los Angeles CA 90033, USA
2 Genetics Squared Inc., 210 South 5th Avenue, Suite A, Ann Arbor MI 48104, USA
3 Department of Internal Medicine, Gundersen Lutheran Medical Center, 1900 South Avenue, La Crosse WI 54601, USA
4 Dipartimento Emergenza e Trapianti d'Organo, Sezione di Urologia, Università di Bari, Piazza G. Cesare 11, Bari 70124, Italy
BMC Cancer 2006, 6:159 doi:10.1186/1471-2407-6-159
Published: 16 June 2006Additional files
Additional file 1:
A comprehensive list of the genes investigated using StaRT-PCR™ including their GenBank accession, GeneID and UniGene cluster numbers.
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Additional file 2:
The box plots for the gene expression values for KDR, MAP2K6 and ICAM1 that are differentially expressed in the primary tumor tissues of the node positive and the node negative classes.
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Additional file 3:
The classifier rules that form the meta-rule generated for node positive patients using the three most frequently used genes, viz. KDR, MAP2K6 and ICAM1.
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Additional file 4:
The performance metrics of the first classifier rule from the meta-rule generated for node positive patients using the three most frequently used genes, viz. KDR, MAP2K6 and ICAM1, on the validation set.
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Additional file 5:
The stage-wise and case-wise performance of the meta-rules generated using 70 genes and the three most frequently used genes, viz. KDR, MAP2K6 and ICAM1, on the validation set. This file also shows the stage- and case-wise performance of the single rule "(MAP2K6/KDR) × (1.0 - (MAP2K6/ICAM1)) > .71" selected from the meta-rule generated using the three most frequently used genes on the validation set.
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