Motivated by the early approaches of Yang et al., Gong et al. high-level features (HLF, e.g. melting point, dimers, molecular weight, etc.), used as independent features in a gradient-boosted decision tree (GBDT). The HLF was based on a vectorization via principal component analysis (PCA). For 6 out of 8 samples, the prediction from the GBDT showed a significant correlation to the actual sensor response. The implementation of HLF improved the prediction but was found to be uniquely correlated to different analytes. Additionally, the authors found that i) different pH conditions lead to different photophysical responses, ii) the stability of the corona phases influences the sensor (more positive sensor response with decreased DNA length, increased adenine content, decreased cytosine content), iii) intensity and shape sensor responses provide orthogonal information, iv) strand-strand interactions play a major role in the organization of the corona phase and v) molecular recognition is mechanistically different for different analytes and experimental conditions.