Sentence examples for more heterogeneous data from inspiring English sources

We found that BEEM and EEM produce relatively similar results, but their performances seem to be different depending on heterogeneity of input transcriptome data: BEEM works better for analyzing more heterogeneous data like the multiple tissue data set.

However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources.

Attempts to use more heterogeneous data sources pose an even greater Big Data challenge.

As compared to Euclidean distance, Sorensen distance retains sensitivity in more heterogeneous data sets and gives less weight to outliers.

Integration of more heterogeneous data has also been utilized in identification of novel disease-associated genes.

Ideally, similar reporting systems should be in place for universities with smaller, more heterogeneous, data holdings.

With more and more heterogeneous raw data becoming available, proper data integration and quality control have become essential for reliable protein network reconstruction, and will be especially important for reconstructing the human protein interaction network.

The increased number of generations allows more time for mutation events and therefore leads to a more genetically heterogeneous data set – that is, one that contains many different genotypes.

Analyzing much more heterogeneous security data can yield significant improvements to the Intrusion Detection domain, and employing Big Data technologies and techniques will allow more of this Big Heterogeneous Data to be utilized.

For instance, in the inherently more heterogeneous human data, only DESeq and limma were able to produce low rates of false positives even if the number of samples was increased.

The value of such a database will only increase as the diversity of images and annotations increases, because the search results and semantic modeling potentially will become more robust and more generalized with training on larger and more heterogeneous image data.