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Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (Danio rerio)
Citation:
WANG, R., D. C. BENCIC, A. D. BIALES, R. W. FLICK, J. M. LAZORCHAK, DAN VILLENEUVE, AND G. T. ANKLEY. Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (Danio rerio). BMC Genomics. BioMed Central Ltd, London, Uk, 13:358, (2012).
Impact/Purpose:
The purpose of this study was to discover gene classifiers for a number of endocrine-disrupting chemicals (EDCs) with different modes of action (MOAs), evaluate their performance, and gain insights on issues important to the discovery process in general, through vigorous computational search and statistical analyses with an experimental design and sampling strategy similar to those found in the current ecotoxicological literature.
Description:
Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all tissue combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset.
