Sentence examples for heterogeneous set of data from inspiring English sources

Here we describe in detail a protein-centric approach for systems integration of such a large and heterogeneous set of data from the NIAID Biodefense Proteomics program, and present scientific case studies to illustrate its application to facilitate the basic understanding of pathogen-host interactions and for the identification of potential candidates for therapeutic or diagnostic targets.

These datasets though are themselves a heterogeneous set of data produced by many different groups (Aza-Blanc et al., 2003; Giddings et al., 2000; Harboth et al., 2003; Holen et al., 2002; Hsieh et al., 2004; Jackson et al., 2003; Kawasaki et al., 2003; Khvorova et al., 2003; Kumar et al., 2003; Reynolds et al., 2004; Ui-Tei et al., 2004; Vickers et al., 2003).

These collections of ubiquitous and heterogeneous sensing devices not only are sources of large volumes of heterogeneous sets of data, but also the amount of computing that is performed collectively on the multitudes of instrumentation/sensor platforms amounts to significant computational powerwhich should be viewed in tandem with that performed in the high-end platforms.

The plant manager wants to identify correlations within these heterogeneous sets of data, but lacks the tools to collect, condition, and analyze the information.

"If you step back at the end of the day, Splunk is able to ingest and correlate heterogeneous sets of data to provide a view into what's happening in their environments," Maraqa said.

As such, the area of machine learning is a popular area of research that can be applied to such heterogeneous sets of data to find patterns for predictive modeling, i.e., training data is used to predict the behavior of the previously unseen test data (Jain 2010).

The centers have generated a heterogeneous set of experimental data using various technologies loosely defined as proteomic, but encompassing genomic, structural, immunology and protein interaction technologies, as well as more standard cell and molecular biology techniques used to validate potential targets identified via high-throughput methods.

The analysis and interpretation of omics data also rely heavily on disparate and heterogeneous sets of biological data.

This method transforms large and heterogeneous sets of expression data into colored images which can be directly compared in terms of similarities and dissimilarities of their textures.

SOM machine learning transforms large and heterogeneous sets of expression data into mosaic images which visualize sample-specific over- and underexpression in terms of characteristic textures.

To describe all intra- and extracellular processes associated with a disease and integrate heterogeneous sets of experimental data, a Quantitative Systems Pharmacology (QSP) modeling approach can be employed.