Abstract: An electronic computing system configured to process a plurality of data elements by performing operations including obtaining information indicative of features of data elements; generating training data for a machine learning engine with the features of the data elements; initializing the machine learning engine with the training data; processing the data elements with the machine learning engine; iteratively updating a model of the data elements based on the processing; and displaying a visual representation of the data elements in accordance with the updated model.

Abstract: A stochastic computational model parameter synthesis system comprising at least one processor capable of executing processor executable code, and a non-transitory computer memory operably coupled with the at least one processor and storing processor executable code, which when executed by processor, causes processor to synthesize at least one parameter of a stochastic computational model to satisfy one or more behavioral specifications of properties observed in a modeled system. The processor generates and searches randomized projections of a first parameter space having n dimensions into one or more second abstract parameter space having d dimensions, where d is less than n, and outputs a signal indicative of a synthesized parameter value to the user.

Abstract: A method performed by one or more processing devices includes retrieving information indicative of compounds and mutations of a target; generating one or more predictions of binding affinities of the compounds to the mutations; generating a simulation for introducing at least one of the compounds in response to at least one of the mutations; identifying, based on an application of a game theory algorithm to the simulation and to the one or more predictions, an equilibrium of the simulation; and identifying, from the equilibrium, a subset of the compounds with an increased binding affinity to the mutations relative to other binding affinities of other of the compounds.

Abstract: A method performed by one or more processing devices includes obtaining information indicative of experiments associated with combinations of targets and compounds; initializing the information with a result of at least one of the experiments; generating, based on initializing, a model to predict effects of the compounds on the targets; generating, based on the model and the experiments obtained, predictions for experiments to be executed; selecting, based on the predictions, one or more experiments from the experiments to be executed; executing the one or more experiments; and updating the model with one or more results of execution of the one or more experiments.