Abstract: The present disclosure generally relates to cancer and methods of treating and diagnosing cancer.

Abstract: Aspects of the present invention relate to a method of estimating a dynamic molecular program of a population of cells including the steps of providing a set of at least two static snapshots of a population of cells undergoing a state transition at a corresponding set of at least two time indices to a neural network, calculating a set of possible population flows between the at least two time indices based on the at least two static snapshots, negatively weighting any of the set of population flows which are unrealistic, and inferring an estimated population flow of the cells between the set of static snapshot data by selecting a population flow from the set of possible population flows with the neural network.

Abstract: Aspects of the present invention relate to a method of determining a gene regulatory system within a cell that transitions from a first state to a second state including providing, to a neural network, a set of single-cell data of a target cell that is transitioning from a first state to a second state, calculating, via the neural network, a continuous trajectory of the target cell from the first state to the second state based on the single-cell data set, and interpolating a gene regulatory system of the target cell based on the calculated continuous trajectory, wherein the gene regulatory system includes a gene expression profile of at least one gene and at least one transcription factor that regulates expression of the at least one gene. Further, a system for determining a gene regulatory profile of a cell comprising at least one neural network is described.

Abstract: The present disclosure generally relates to cancer and methods of treating and diagnosing cancer.