Class prediction in toxicogenomics.

Nandini Raghavan; Dhammika Amaratunga; Alex Y Nie; Michael McMillian

doi:10.1081/bip-200048836

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Class prediction in toxicogenomics.

Nandini Raghavan·Dhammika Amaratunga·Alex Y Nie·Michael McMillian

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Abstract

The intent of this article is to discuss some of the complexities of toxicogenomics data and the statistical design and analysis issues that arise in the course of conducting a toxicogenomics study. We also describe a procedure for classifying compounds into various hepatotoxicity classes based on gene expression data. The methodology involves first classifying a compound as toxic or nontoxic and subsequently classifying the toxic compounds into the hepatotoxicity classes, based on votes by binary classifiers. The binary classifiers are constructed by using genes selected to best elicit differences between the two classes. We show that the gene selection strategy improves the misclassification error rates and also delivers gene pathways that exhibit biological relevance.

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Algorithms Chemical and Drug Induced Liver Injury Data Interpretation, Statistical Discriminant Analysis Gene Expression Linear Models Oligonucleotide Array Sequence Analysis Predictive Value of Tests RNA, Messenger Toxicogenetics Journal Article

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Nandini Raghavan, Dhammika Amaratunga, Alex Y Nie, Michael McMillian. 2005. Class prediction in toxicogenomics.. https://doi.org/10.1081/bip-200048836

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