Sentence examples for latent relationship between from inspiring English sources

We further discover the latent relationship between the OS and BS, and the latent relationship between OS and BS with MSCA combined the knowledge map technology.

With gene expression data and averaged motif frequency matrix of corresponding genes, it is natural to fit a regression model to discover the latent relationship between motifs as variables and expression as response.

Metabonomics involves the analysis of endogenous metabolites of various biofluids and tissues, and harvests a latent relationship between the changed metabolic profiles and the physiological status of the biosystems [ 14].

Principal component analysis provided a good spatial overview of the essential information contained in backscatter and coherence time series and revealed latent relationships between both time series and the surface temperature.

The latent relationships between these constructs are presented in the following section.

An alternative, and orthogonal, approach is to integrate individual histone modification maps to discover latent relationships between epigenetic marks.

A structural equation model was established in order to analyse the latent relationships between real-life creativity measures (creative activities and creative achievements), creative potential (ideational fluency and originality), openness to experiences, and intelligence.

For query semantic extension, latent semantic analysis is adopted to discover the latent semantic relationships between queries and ontology semantic features, and ontology semantic graph is used to represent the query.

In this paper, we extend our earlier work in [25] to systematically capture time information as well as latent (deep) relationships between a source speaker's and a target speaker's features in a single network.

The measurement model for the endogenous latent variables stipulates the relationship between the endogenous latent variables and the corresponding manifest variables.

The population model, which describes the distribution of the latent variables and the relationship between the contextual variables and the latent variables, is a multivariate multiple regression model, where γ are the regression coefficients that are estimated, Σ is the conditional covariance matrix, and W are the contextual variables.