Groups that have evaluated the utility of clinical and laboratory biomarkers to assess risk of preeclampsia (21–24) have reported moderate-high results (AUC between 0.80 and 0.90) when using such data within machine learning or neural network algorithms as shown by Jhee et al., Marić et al., Neocleous et al. and Li et al. (8, 9, 20, 25) At the same time, there has been considerable interest and research into the role of PlGF and sFlt-1 in preeclampsia testing (26). The gene discussed is PGF; the disease is preeclampsia.