Abstract: A system is provided including a data pipeline and a model pipeline. A data pipeline includes: an input that receives a first dataset representing categorical features and a second dataset representing numerical features; a feature ingestion block that generates an output corresponding to a sum of the first dataset with the second dataset; an output that provides training labels based on a processing of the summed datasets to predict a temporally isolated and discrete event; and a label creation block that receives the output and generates labels for date features in the first dataset. A model pipeline includes a neural network(s) that: receives a first input corresponding to a summation of non learned date embedding with learned feature embedding; and contextualizes the summation by date embedding historical patient data into the summation. The model pipeline includes a prediction block that receives the contextualized summation and predicts one or more outcomes.

Abstract: Systems and methods for rapid gene set enrichment analysis and applications thereof are described. These systems and methods enable improved prioritization of relevant gene sets while maintaining a relatively lower false positive rate. Additionally, the ability to accelerate enrichment analysis and analyze hundreds of thousands of gene sets is described.

Abstract: Systems and methods for rapid gene set enrichment analysis and applications thereof are described. In certain embodiments, the systems and methods described herein may be used to identify one or more candidate therapies for treatment of a disease (e.g., cancer). These systems and methods enable improved prioritization of relevant gene sets while maintaining a relatively lower false positive rate. Additionally, the ability to accelerate enrichment analysis and analyze hundreds of thousands of gene sets is described.