Sentence examples for natural data sets from inspiring English sources

Both artificial and natural data sets are used for the demonstration.

The proposed model was also applied to natural data sets of the seismic velocity structures in the mantle wedge beneath northeastern Japan, under the assumptions that the fluid was melted and the temperature and petrologic structures were uniformly distributed.

Unfortunately, the underlying structure of these natural data sets is often fuzzy, and the computational identification of data clusters generally requires (human) expert knowledge about cluster number and geometry.

Therefore, the MDR is unsuitable for the majority of natural data sets which commonly belong to imbalanced cases and controls.

Instead of covering many different parameter combinations, we used the parameters of two very different natural data sets for the simulations.

Our analyses on simulated and natural data sets demonstrate that fastSTRUCTURE estimates approximate posterior distributions on ancestry proportions 2 orders of magnitude faster than STRUCTURE, with ancestry estimates and prediction accuracies that are comparable to those of ADMIXTURE.

We use the maq (maq.sourceforge.net) simulation tool to generate multiple synthetic read sets from the human genome, of the same size as our natural data

A chi-square test indicates that biotic traits are over-represented in the natural data set, but under-represented in the laboratory data set (p < 2.2e-16 2.2e-16ect size and p = 0.004382 for effect direction).

In the example of a natural data set with an unbalanced HT group and non-HT group, the unbalanced function based MDR algorithm is able to detect the significant association with hypertension in terms of 2- to 8-locus models by their OR and Boot-OR values (Table 4).

In contrast, the natural product data sets MEGx and NATx are, relative to the other data sets, the least diverse considering MACCS keys and molecular scaffolds.

The purpose of this study was to explore the predominant determinants of shower end use consumption and to find an overarching research design for building a residential water end use demand forecasting model using aligned socio-demographic and natural science data sets collected from 200 households fitted with smart water meters in South-east Queensland, Australia.