Identification of toxicologically predictive gene sets using cDNA microarrays
Autor: | Kevin R. Hayes, Stevan B. Jovanovich, E.W.N. Glover, Russell S. Thomas, Gina M. Zastrow, Tomi Silander, Christopher A. Bradfield, Janardan K. Reddy, Kalyan Pande, David R. Rank, Sharron G. Penn, Mark Craven |
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Rok vydání: | 2001 |
Předmět: |
Male
Drug-Related Side Effects and Adverse Reactions Gene Expression Mice Predictive Value of Tests Gene expression Animals Oligonucleotide Array Sequence Analysis Pharmacology Genetics CDNA Microarrays biology Peroxisome proliferator Gene Expression Profiling Gene sets Aryl hydrocarbon receptor Mice Inbred C57BL Gene expression profiling Pharmaceutical Preparations Models Animal biology.protein RNA Molecular Medicine Identification (biology) RNA biosynthesis Signal Transduction |
Zdroj: | Molecular Pharmacology. 60:1189-1194 |
ISSN: | 1521-0111 0026-895X |
DOI: | 10.1124/mol.60.6.1189 |
Popis: | 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. |
Databáze: | OpenAIRE |
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