Sentence examples for model data such from inspiring English sources

The RuleDataService is used as a persistent container for reusable model data such as Rules and Facts.

A more ambitious challenge lies in the need to be able to efficiently deal with the steady stream of updates to model data (such as genomic references), bioinformatics tools and analysis procedures.

Together with other functional evidence and results such as animal model data, such observations have led to the localization of causal genes and pathways, improving knowledge of the aetiology of this multifactorial disease.

The user can also adjust key model data parameters such as the population BRCA1 and BRCA2 mutation frequencies and mutation search sensitivities, so that the risk calculation can be tailored to different populations and genetic testing methods.

This feature makes the GMBM a good candidate to model complex data such as textured images or multifractal processes.

PLS is typically used to model spectral data such as near infrared or 2D-fluorescence maps [ 37].

Estimating equations, particularly in the form of generalized estimating equations [ 14], have become popular in situations where it is difficult to model complex data, such as correlated data that do not arise from a multivariate normal distribution [ 11, 15].

Additionally, POTION also contains exclusive features to further model user data, such as the specification of additional start/stop codons and the removal of entire groups based on phylogenetic and quality criteria.

It is a reliable resource for the model developers as it manually curates the model-related data, such as kinetic rate parameters, protein concentration, and dynamic rate equations, from the published literatures.

Though HMM has been widely used in modelling correlated data, such as DNA copy number data, it is widely known to be computationally very slow, especially for the analysis of high dimensional data such as the aCGH data.

We will use log-binomial (prevalence data), linear regression (continuous outcomes after log-transformation), or negative binomial models (count data such as STH counts) to compare disease risk between intervention and control areas.