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Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor alpha (ER alpha), often modulated by potential endocrine disrupting chemicals. ER alpha biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ER alpha agonists and 3 ER alpha antagonists in ER alpha-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ER alpha as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ER alpha-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ER alpha activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ER alpha signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ER alpha gene expression biomarker can accurately identify ER alpha modulators in large collections of microarray data derived from MCF-7 cells.