Directed acyclic graph (DAG) was used to depict the causal structure.

Selected set of relations resolving individual taxa pairs is subsequently used to construct a directed acyclic graph (DAG).

A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions.

A directed acyclic graph was used to encode relationships between predictors, outcomes, and covariates and to guide the choice of covariates to control for confounding.

Directed acyclic graphs (DAG) were used to depict the underlying causal structure and guide analysis.

Directed acyclic graphs, such as trees, have been widely used to model top-down statistical dependences as a prior for improved image segmentation.

Each individual map is thus a partial order defined on the set of markers, and can be represented as a Directed Acyclic Graph (DAG).