Examples of assigning nodes to tiers are given below. Additionally, a tutorial with examples of using the structural constraint interface is available here.
Example 1: A genetic network linking genotype and phenotype
Bayesian network modeling can be used to understand the biological processes that link genotype with phenotypes. For example, consider a dataset than contains three types of data: genotypes; molecular level phenotypes, such as gene expression traits; and higher order phenotypes. This network could be organized into three tiers. Tier 1 would contain the genotypes, Tier 2 would contain the molecular level phenotypes, and Tier 3 would contain the higher order phenotypes. In many cases, biologically relevant network models start with genotypes and terminate with higher order phenotypes, and the BNW structural constraint interface can be used to include this prior knowledge when performing structure learning. By default, the structural constraint interface would allow directed edges from genotype (Tier 1) nodes to both types of phenotype nodes (Tier 2 and Tier 3) and from the molecular level phenotypes (Tier 2) to the higher order phenotypes (Tier 3). It would also allow directed edges between nodes in the same tier. Depending on the specifics of their dataset, users may want to prevent directed edges between nodes within Tier 1 (i.e., prevent innate genotype nodes from being caused by other genotype nodes) or prevent Tier 1 nodes from being the immediate parents of Tier 3 nodes, which would require that molecular level phenotypes intervene between the genotypes and higher order phenotypes.
Example 2: Time series data
While it does not fully support dynamic Bayesian network modeling, BNW can be used for modeling of time-series datasets and time-order information can be incorporated with the structural constraint interface. Data at each time point can be grouped into a tier and these tiers can be used to provide constraints on the network structure. For example, data from the second time point can be placed in a tier that cannot be the parents of a tier containing data from the first time point but can be the parents of tiers containing data from all subsequent time points.