FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.
Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research
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A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data
FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.