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Vol. 60, Issue 6, 1189-1194, December 2001
McArdle Laboratory for Cancer Research (R.S.T., G.M.Z., K.R.H.,
K.P., E.G., C.A.B.) and Department of Biostatistics and Medical
Informatics (M.W.C.), University of Wisconsin Medical School, Madison,
Wisconsin; Aeomica, Sunnyvale, California (D.R.R., S.G.P.); Department
of Computer Science, University of Helsinki, Teollisuuskatu, Finland
(T.S.); Department of Pathology, Northwestern University Medical
School, Chicago, Illinois (J.K.R.); and Advanced Research Team,
Molecular Dynamics Inc., Sunnyvale, California (S.B.J.)
We have developed an approach to classify toxicants based upon their
influence on profiles of mRNA transcripts. Changes in liver gene
expression were examined after exposure of mice to 24 model treatments
that fall into five well-studied toxicological categories: peroxisome
proliferators, aryl hydrocarbon receptor agonists, noncoplanar
polychlorinated biphenyls, inflammatory agents, and hypoxia-inducing
agents. Analysis of 1200 transcripts using both a correlation-based
approach and a probabilistic approach resulted in a classification
accuracy of between 50 and 70%. However, with the use of a forward
parameter selection scheme, a diagnostic set of 12 transcripts was
identified that provided an estimated 100% predictive accuracy based
on leave-one-out cross-validation. Expansion of this approach to
additional chemicals of regulatory concern could serve as an important
screening step in a new era of toxicological testing.
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