Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio).

ABSTRACT:

Background

Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and 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 dedic

SUBMITTER: Wang RL 

PROVIDER: S-EPMC3469349 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature