Based on the premise that a high performing sequence is likely obtained from a set of completely randomized sequences, Kelich et al. used a SELEC approach to narrow down a DNA-pool of ≈ 10 10 DNAsequences down to 100 sequences and train CNN models with single-wavelength data from these sensors. The authors showed that machine learning methods can classify (Figure ) and predict (Figure ,c) promising DNA sequences. Additionally, the authors demonstrated separately, that a simpler PCA approach appears to be predictive in analysis plots, but exhibits a poor correlation between predictions and validation experiments. Overall, the authors identified five serotonin sensors with higher responses than those previously identified in experimental screening approaches.