Sentence examples for specification of interactions from inspiring English sources

To restrict the set of admissible project roadmaps, the planning model allows the specification of interactions among projects and domain-specific constraints that project roadmaps must not violate.

This paper provides formal specification of interactions in typical public health surveillance systems involving healthcare agencies at local, state and federal levels.

Amato et al. [ 4] have provided a mathematical model for the specification of interactions between genetic and environmental risk factors.

In our statistical specification of interactions, we modeled disease risk due to the combination of genotypic and environmental factors as different than the sum of the risks of each factor alone (Khoury et al. 1988; Thomas 2010).

It can study any list of predictors (binary, categorical, continuous), requires no explicit structural specification of the model, no specification of interactions, and is scalable to very large sets of predictors, for example over a million single nucleotide polymorphisms (SNPs) in genome-wide association studies.

With the rise of electronic integration between organizations, the need for a precise specification of interaction behavior increases.

By default, for better data interchangeability, ExprEssence recognizes Systems Biology Ontology terms [ 34], also included in the activity flow language of the Systems Biology Graphical Notation (SBGN, [ 35]), for the specification of interaction types.

One of the major challenges in this field appears to be explicit formal specification of such interactions.

RFPM also provides accurate estimates of the probabilities, but without any explicit specification of any interactions.

While not all cities go through the entire life cycle, a comprehensive theory of cities and specific policies need to include specification of the interaction of the various forces that shape the entire range of urban patterns and identification of specific combinations of values that create phase transitions among these patterns.

C&RT makes no assumption of a monotonic or parametric relationship with the outcome, is able to identify complex interactions among the predictor variables without a priori specification of the interaction terms, and can handle datasets where the number of predictors is high relative to the number of observations.