Sentence examples for integrative data 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.

With COSMIC, which is currently integrating TCGA and IARC TP53 information, and the RCGDB, which already integrates most of in this review presented data sources, two promising integrative data resources are available.

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

Additionally, we discuss generic issues relevant to the integration of clinical and biomolecular data, as well as recent developments in integrative data analyses including biomolecular network reconstruction and mathematical modeling.

Integrative data analysis reveals that a total of 37 genes in 24 transcription units (TUs) belong to OmpR regulon.

The originality of the work reported here consists in the specific screen of a set of 714 kinases and, using integrative data-mining analyses to filter functional kinase groups, constructing kinase interaction networks that successfully identify new biologically relevant targets.

As a result, the analysis of both single and integrative omics data is prone to high rates of false-positives due to chance alone.

To reach these goals, network construction relies heavily on integrative omics data and accumulated knowledge that begin with combining diverse datasets into networks using informatics tools.

This is done in both the "transcriptome only" (as introduced in Sect.  2) and the integrative oncogenomics data (as introduced in Sect. 3).

This approach aims to advance the researches based on integrative empirical data emanating from independent sources, as well as having its influence on researchers seeking to make novel predictions regarding the immune system response to infectious diseases.