Sentence examples for sample to classify from inspiring English sources

We show that using simple byte-value histograms we retain enough information about the opcodes within a sample to classify the target architecture with high accuracy, and then discuss heuristic-based features that exploit information within the operands to determine endianess.

Objective: We characterized the variability of urinary phthalate metabolite and BPA concentrations before and during pregnancy and the ability of a single spot urine sample to classify average gestational exposure.

However, the potentially episodic nature of exposure, combined with the short biological half-lives of these compounds raises questions about the adequacy of a single spot urine sample to classify gestational BPA and phthalate exposure.

Statistical analyses were performed to determine the between- and within-subject variance apportionment, and the sensitivity and specificity of a single urine sample to classify a subject's 3-month average exposure.

After combining contingency tables for all subjects with three or more samples, we calculated sensitivity, specificity, and positive predictive value for the ability of a given single urine sample to classify a subject in the highest BPA tertile.

This information will help inform exposure assessment in epidemiological studies and aid in the evaluation of human studies that use a single spot urine sample to classify these exposures.

Therefore, this investigation was designed to identify a proteomic profile in human hepatic tumor samples to classify patients with CRLM as "mild" or "severe" based on the 5-year survival.

It is interesting to see in Fig. 7a and b that the random selection of samples to classify in the active learning presents better results than the statistical techniques.

We evaluated the sensitivity and specificity of using a single specimen versus repeat samples to classify a woman's exposure in the low or high category.

We used the criteria of genes with an RPKM value of at least 1 in all five samples to classify genes as either active or inactive.

In this paper, we have proposed a new SR-based method for tumor classification which uses the noiseless salient features extracted from the original samples to classify a test sample.