Sentence examples for integrative data analysis from inspiring English sources

Additionally, following computational approaches for data integration are reviewed: data aggregation, integrative data analysis including methodological aspects as well as examples, biomolecular pathways and network reconstruction, and mathematical modelling.

Properties such as easy access, large datasets management, modularity, integration with other tools, etc, are important for performing large-scale integrative data analysis with methods and visual techniques from different (possibly integrated) tools.

Once the data are integrated, that is, preprocessed and deposited in a dedicated database, tools for integrative data analysis can be applied.

Motivated by our recent results of the transcriptomic characterization of the L. longipalpis pheromone gland, we performed a proteomic analysis of this tissue combining SDS-PAGE, and mass spectrometry followed by an integrative data analysis.

Integrative data analysis, the process of pooling and analyzing data from multiple studies, produces larger and more heterogeneous samples with which to evaluate measurement equivalence across the full continuum of smoking quantity and frequency.

Big data may provide sufficient data to study subpopulations that are underrepresented in traditional samples, such as heroin addicts, using techniques such as integrative data analysis that combine independent data sets to product adequate sample sizes (Srinivasan et al. 2015; Curran and Hussong 2009).

Known variously as 'integrative data analysis' [ 4], 'mega-analysis' [ 5], 'cross-study analysis' [ 6], 'individual participant/patient data analysis' [ 7, 8] and even 'individual participant/patient data meta-analysis' [ 2, 9- 13], the combination of raw data from multiple studies into single analysis sets is increasingly undertaken.

Integrative genomic data analysis, along with clinical and pathological information allowed us to identify prognostic and subtype-specific miRNAs, to elucidate whether they were affected by DNA genomic aberrations and to monitor their associative and/or functional relationships with the activity of biological pathways.

Deciphering the large amount of Toxoplasma transcriptomic data currently available requires integrative approaches to data analysis that incorporate experimental data and bioinformatics analysis.

This comparison highlights some of the limitations associated with statistical analyses that rely on a single source of evidence to derive conclusions on biological function, and argues for the need for integrative approaches to data analysis.

We also developed integrative methods for data analysis that use correlation analysis, metabolic topology, and transcription factor enrichment to interrogate and characterize the complex relationships that arise between gene expression levels, metabolite levels, lipid levels, and reaction fluxes across multiple conditions.