Representing and learning temporal relationships among experimental variables

The authors describe the necessity to capture temporal information in scientific experiment design for analysis by machine learning algorithms that can learn useful temporal patterns among experimental variables. They have identified three types of temporal information, namely duration, rate of change, and sequence of application of laboratory operators that are useful to learn from experimental data. Their motivation stems from study of experimental design in the domain of macromolecular crystallography. They identify the challenges posed both by the domain as well as the temporal information on machine learning programs, and describe work in progress. They outline the method of temporal specialization for inducing temporal relations between experimental variables, and illustrate with an example from the domain.