Sentence examples for modeling complex interactions from inspiring English sources

In contrast to ANOVA, GEMANOVA is especially suited for modeling complex interactions and making easily understandable models of these.

By focusing on regions with just one AluY insertion, we avoid modeling complex interactions between multiple AluY insertions in one or several inter-SNP intervals.

Individual-based models provide powerful tools to model complex interactions characterized by individual variability.

The application possibilities and limitations of the model as well as challenges to model complex interactions in land use systems are discussed.

There exists a continuous trade-off between modelling complex interactions with the required level of detail and keeping the model or framework simple and comprehensible for the recipients of the results and for the modellers themselves.

In contrast to classical statistical modeling with test of significance, machine learning uses a hypothesis-free approach that allows us to model complex interactions driven by the data [100].

Genetic regulatory networks seek to model complex interactions and dynamics of gene regulations.

When compared with other methods, RF shows a very high accuracy, an ability to model complex interactions among predictors, and the flexibility to perform several types of statistical analysis [ 37].

Although a correlation exists, there are still differences in basal expression which may be associated with the origins of the models (i.e. normal hepatic tissue versus hepatoma derived Hepa1c1c7 cells), as well as the inability of in vitro systems to effectively model complex interactions between different cell types (e.g. Kupffer and stellate cells).

BRT combines the strengths of regression trees (i.e., the omission of irrelevant variables and the ability to model complex interactions) with machine learning techniques (i.e., the building of an ensemble of models that approximate the true response surface [ Elith and Leathwick, 2009]).

BioScape is based on the Stochastic Pi-Calculus, and it is motivated by the need for individual-based, continuous motion, and continuous space simulation in modeling complex bacteria-materials interactions.