Sentence examples for data for distinguishing from inspiring English sources

WGS is of particular interest because it provides definitive data for distinguishing outbreak isolates from nonoutbreak isolates in common and highly clonal populations (Allard et al. 2012, 2013).

In sequencing datasets even for current large experiments, the numbers of PTVs in any gene will tend to be small, meaning that there are only few data for distinguishing the null and alternative models.

The two most used methods to analyse 18F-FDG PET data for distinguishing benign from malignant lung nodules are either by subjectively assessing FDG avidity in comparison to background or using semi-quantitative analyses, SUV, most commonly SUVmax, with variable thresholds used as a cut-off to separate benign from malignant disease.

Moreover, there would appear to be a need for efficient and standardized analytical methods for extracting relevant information from actigraphy data and for distinguishing different subtypes of mood disorder recordings [ 44, 45].

DF was applied to the proteomic data set for distinguishing cancer from noncancer.

In such a case, seismic data is useful for distinguishing the events since the absence of volcanic seismicity rules out eruptive events.

Scholars should corroborate the findings presented herein using different estimation strategies and alternative data that allows for distinguishing the remittance behavior of emigrants, the spending patterns of receivers, the extent to which remittances and cash assistance are fungible, and the electoral participation patterns of receivers.

A receiver operator characteristic (ROC) analysis indicated the citrate had 100% specificity at 86% sensitivity and an overall 88% accuracy [see Additional data file 1] for distinguishing between class III/IV and class V LN patients.

Our data also support the use of protein interaction data to set thresholds for distinguishing hits from non-hits in large-scale RNAi-based screens, as has been previously suggested [ 109, 110].

In total, the result of grouping using Japanese geomorphological and geological data is generally suitable for distinguishing lowlands, terraces, debris slopes, hills, bedrock mountains, and large slopes, especially those formed by Holocene mafic volcanos.

The idea behind the distance-based features is to extend and take the advantage of the centroid-based classification approach [5], i.e., all the centroids over a given dataset usually have their discrimination capabilities for distinguishing data between different classes.