Sentence examples for multiple classes of data from inspiring English sources

This paper considers optimization of time average admission rate in an energy-constrained network system with multiple classes of data flows.

Multiple classes of data were compared by ANOVA, and pair-wise comparisons by Student's t-test.

Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons.

This insight may be particularly helpful to future studies involving large data sets with multiple classes of genomic information, for example the Cancer Genome Atlas (TCGA) or the International cancer Genome Consortium (ICGC) that are relying on ever higher-resolution next-generation sequencing approaches to stratify patients and identify disease-specific driver mutations.

Relying on the TCGA CRC data, we conducted a supervised analysis, integrating all of the multiple classes of available genomic feature data.

In this first study, we have shown how the gene sequence classifier Strand can be used to predict multiple classes of polymorphic malware using data provided by the Kaggle Microsoft Malware Classification Challenge (Big 2015).

The proposed system has three key components: 1) an API (Application Programming Interface) that extracts location information using Google Street View platform, 2) a computer vision method that is capable of detecting and classifying multiple classes of traffic signs; and 3) a data mining method to characterize the data attributes related to clusters of traffic signs.

The database has over 18,000 data points from multiple classes of compounds, whose relevant properties have been measured at several temperatures.

We conducted a regularized elastic-net regression analysis of heterogeneous genomic data that encompassed multiple classes of genetic aberrations (i.e. genomic features) in colorectal cancer.

Hybridization intensity data is subject to multiple classes of noise that can be attenuated by normalization procedures.

The pesticide breakout group concluded the epidemiological, animal, and mechanistic data support the biological plausibility that exposure to multiple classes of pesticides may affect risk factors for diabetes and obesity, although many significant data gaps remain.