Sentence examples for graphical networks from inspiring English sources

Gaussian graphical networks have the ability to calculate only direct correlations while indirect correlations are not taken into account.

Recent advances in bioinformatics, data and text mining allow integrating experimental data in the form of user-defined graphical networks.

The graphical networks will assist in the understanding of the role of the PTMs.

Most methods based on graphical networks aim at finding out weak points in the network.

Search results can be displayed as both tables and graphical networks.

In cognitive science, for example, Bayesian graphical networks are widely used to study structure learning in causal induction [95,96].

To reason about unexpected observations and their correlations across poses, we augment the Dynamic Bayesian Network for Markov localization to include varying edges and nodes, resulting in a novel Varying Graphical Network representation.

In this paper we propose how to select and model semantic based tweet data, so to compare and integrate them with market data, within the framework of graphical network models.

Having introduced a method aimed at selecting financial tweets, we now introduce the graphical network models that will be used to estimate relationships between N banks, both with market and tweet data.

The major nodes or groups for the networks' architecture, transmittal status, and information were identified using graphical network mapping, quantitative analysis of subjective impressions, and quantified statistical analysis using Sociometric Statue and Centrality.

An integrative graphical network model was constructed by combining genomic, gene pathway, lipidomic, and physiological endpoint results to better understand mechanisms of liver toxicity and physiological endpoints affected by these compounds.