Abstract: Embodiments of the disclosure include implementing a ML-enabled cellular disease model for validating an intervention, identifying patient populations that are likely responders to an intervention, and developing a therapeutic structure-activity relationship screen. To generate a cellular disease model, data is combined from human genetic cohorts, from the literature, and from general-purpose cellular or tissue-level genomic data to unravel the set of factors (e.g., genetic, environmental, cellular factors) that give rise to a particular disease. In vitro cells are engineered using the set of factors to generate training data for training machine learning models that are useful for implementing cellular disease models.

Abstract: Prediction of allograft rejection is provided based on the quantification of transplant-derived circulating cell-free DNA (dd-cfDNA levels) in the absence of a donor genotype. The technology provided herein alleviates some of the barriers to the implementation of Genome Transplant Dynamics (GTD), which will further widen its clinical application.