Abstract
Of 47 endocrine-disrupting chemicals (EDCs) collected from literature and related to breast cancer, not all were tested in a toxicity forecaster (ToxCast) program of the US-Environmental Protection Agency (EPA). Therefore, deep learning models based on the toxicity data in that database have been used to predict the molecular toxicity of the untested EDCs. Combined with the values of median lethal doses (LDs), six potential targets of EDCs related to breast cancer have been identified, viz. MYC proto-oncogene, urokinase plasminogen activator receptor (PLAUR), cytochrome P450 4 A 11, nuclear receptor 1 H 2 (NR1H2), peroxisome proliferator-activated receptor alpha (PPARA), and hypoxia-inducible factor 1 alpha (HIF1A).
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