Abstract: Systems and methods for reducing a number of test objects in a test object dataset are provided. A target model with a first computational complexity is applied to a subset of test objects from the test object dataset and a target object, thereby obtaining a subset of target results. A predictive model with a second computational complexity is trained using the subset of test objects and the subset of target results. The predictive model is applied to the plurality of test objects, thereby obtaining a plurality of predictive results. A portion of the test objects are eliminated from the plurality of test objects based at least in part on the plurality of predictive results. The method determines whether one or more predefined reduction criteria are satisfied. When the predefined reduction criteria are not satisfied, an additional subset of test objects and target results are obtained, and the method is repeated.

Abstract: In one embodiment, one or more processor-readable media storing code representing instructions that when executed by one or more processors cause the one or more processors to receive a set of parameter values defining a boundary within a data space associated with a dataset. The dataset can represent signaling related to a test substance. A first metric can be defined based on a first portion of the dataset associated with a first region defined by the boundary. A second metric can be defined based on a second portion of the dataset associated with a second region defined by the boundary after the boundary is modified. The second region can be different than the first region.