Deciphering DNA nucleotide sequences and their rotation dynamics with interpretable machine learning integrated C ₃ N nanopores

N nanopores. The optimized eXtreme Gradient Boosting Regression (XGBR) algorithm has predicted the fingerprint transmission of each unknown nucleotide and their rotation dynamics with root mean square error scores as low as 0.07. Interpretability of ML black box models with the game theory-based SHapley Additive exPlanation method has provided a quasi-explanation for the model working principle and the complex relationship between electrode-nucleotide coupling and transmission. Moreover, a comprehensive ML classification of nucleotides based on binary, ternary, and quaternary combinations shows maximum accuracy and F1 scores of 100%. The results suggest that ML in tandem with a nanopore device can potentially alleviate the experimental hurdles associated with quantum tunneling and facilitate fast and high-precision DNA sequencing.