Abstract: A high density correlation system can train a machine-learned model to determine one or more phenotypes of a cell and identify compounds corresponding to a user-queried phenotype. The high density correlation system can generate training data using single-cell images and train the machine-learned model using the generated training data. The machine-learned model can determine phenotypes of cells based on images of the cells. The high density correlation system can generate a database that includes phenotype-compound mappings generated based on the outputs of the machine-learned model. After receiving a query that identifies a phenotype, the high density correlation system can generate a result set of the query using the database for display at a graphical user interface (GUI). The result set can identify compounds corresponding to the identified phenotype. Additionally, the displayed compounds can be ordered based on a score for each compound.

Abstract: Disclosed herein are compositions and methods for increasing lifespan, for preventing or treating a disease including an aging-related disorder, for reducing a symptom of aging, and/or boosting an immune system in a mammal. Also disclosed herein are compositions and methods for improving effectiveness of a vaccine in a mammal. The compositions comprise, at least, a therapeutically effective amount of disulfiram and one or more additional ingredients.

Abstract: Disclosed herein are methods and systems for identifying a drug capable of changing a cell's state, function, and/or predicted age, which is useful in, at least, drug discovery. Further disclosed herein are methods and systems for identifying an in vitro cell's state, function, and/or predicted age.

Abstract: Disclosed herein are compositions and methods for increasing lifespan, for preventing or treating a disease including an aging-related disorder, for reducing a symptom of aging, and/or boosting an immune system in a mammal. Also disclosed herein are compositions and methods for improving effectiveness of a vaccine in a mammal. The compositions comprise, at least, a therapeutically effective amount of disulfiram and one or more additional ingredients.

Abstract: Disclosed herein are compositions and methods for increasing lifespan, for preventing or treating a disease including an aging-related disorder, for reducing a symptom of aging, and/or boosting an immune system in a mammal. Also disclosed herein are compositions and methods for improving effectiveness of a vaccine in a mammal. The compositions comprise, at least, a therapeutically effective amount of disulfiram and one or more additional ingredients.

Abstract: Disclosed herein are compositions and methods for increasing lifespan, for preventing or treating a disease including an aging-related disorder, for reducing a symptom of aging, and/or boosting an immune system in a mammal. Also disclosed herein are compositions and methods for improving effectiveness of a vaccine in a mammal. The compositions comprise, at least, a therapeutically effective amount of disulfiram and one or more additional ingredients.

Abstract: Disclosed herein are compositions and methods for increasing lifespan, for preventing or treating a disease including an aging-related disorder, for reducing a symptom of aging, and/or boosting an immune system in a mammal. Also disclosed herein are compositions and methods for improving effectiveness of a vaccine in a mammal. The compositions comprise, at least, a therapeutically effective amount of disulfiram and one or more additional ingredients.

Abstract: The present disclosure provides systems, media, and methods for applying machine learning to determine the biological age of a sample. A method of using a biological age predictor may comprise: (a) providing data of a sample to the biological age predictor; (b) treating the sample with a substance, thereby generating a treated sample; and (c) providing data of the treated sample to the biological age predictor. The biological age predictor may generate the first biological age and/or the second biological age.

Abstract: The present disclosure provides systems, media, and methods for applying machine learning to determine the biological age of a sample. A method of using a biological age predictor may comprise: (a) providing data of a sample to the biological age predictor; (b) treating the sample with a substance, thereby generating a treated sample; and (c) providing data of the treated sample to the biological age predictor. The biological age predictor may generate the first biological age and/or the second biological age.