Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells. The data are intended to improve toxicity prediction, per category, of various compounds through the use of support vector machines, and by applying gene networks. The accuracy of our system was 97.5-100% in three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs), and non-genotoxic carcinogens (NGCs). We predicted that two uncategorized compounds (bisphenol-A and permethrin) should be classified as follows: bisphenol-A as a non-genotoxic carcinogen, and permethrin as a neurotoxin. These predictions are supported by recent reports, and as such constitute a good outcome. Our results include two important features: 1) The accuracy of prediction was higher when machine learning was carried out using gene networks and activity, rather than the normal quantitative structure-activity relationship (QSAR); and 2) By using undifferentiated ES cells, the late effect of chemical substances was predicted. From these results, we succeeded in constructing a highly effective and highly accurate system to predict the toxicity of compounds using stem cells.
Keywords: gene network; human embryonic stem cell; toxicity prediction.