Abstract: The present disclosure provides automated methods and systems for implementing an aging analysis pipeline involving the training and deployment of a predictive model for predicting cellular ages of cells. Such a predictive model distinguishes between morphological cellular phenotypes e.g., morphological cellular phenotypes elucidated using Cell Paint, exhibited by cells of different ages. The predictive model is further useful for developing new cellular aging assays that include biomarkers that heavily contribute towards predictions of the predictive model. Furthermore, the predictive model is useful for screening drug candidates for their ability to alter or suppress age related phenotypes.

Abstract: The present disclosure provides automated methods and systems for implementing a pipeline involving the training and deployment of a predictive model for predicting cellular diseased state (e.g., neurodegenerative disease state such as presence or absence of Parkinson's Disease) and for identifying features specific to a disease. Such a predictive model is trained by using training data generated from at least one cohort of synthetically pooled cells of a known disease state.

Abstract: The present disclosure provides automated methods and systems for implementing a pipeline involving the training and deployment of a predictive model for predicting cellular diseased state (e.g., neurodegenerative disease state such as presence or absence of Parkinson's Disease). Such a predictive model distinguishes between morphological cellular phenotypes e.g., morphological cellular phenotypes elucidated using Cell Paint, exhibited by cells of different diseased states.