Abstract: There is risk or uncertainty that a predictive model, such as a machine learning model, that has been fitted to a dataset of known cases will not produce the same distribution of predictions or outcomes when applied to future cases. To assess risk, the dataset of known cases is statistically assessed using best-fit probability distributions and correlations for one or more features of the dataset, then new cases are generated to produce a synthetic dataset that has statistical characteristics similar to the known dataset. The predictive model can be applied to the synthetic dataset. A comparison of the distribution of predictions by the predictive model on the known dataset and on the synthetic dataset can be made. Significant variations in the distribution of predictions or outcomes can indicate that the model is not suitable for future cases. Lack of such variations can increase confidence and willingness to use the model.

Abstract: Methods and compositions for use in selectively expressing a protein in a telomerase expressing cell are provided. In the subject methods, an expression cassette comprising a Site C repressor site and a coding sequence for the protein is introduced into the target telomerase expressing cell, e.g., by administering the expression cassette to a host that includes the target cell. The protein may be a therapeutic or diagnostic protein. The subject methods find use in a variety of different applications, and are particularly suited for use in diagnostic and therapeutic applications, e.g., of cellular proliferative disease conditions.