Since Metabolon measured datasets were extensively used in this paper, they are further illustrated in Fig. 1c.
The CT and ΔΔCT values, along with the final RT-PCR measured gene expression datasets used in our analysis are provided in Additional file 2: Table S2.
To maximise future utility and reproducibility of the measure the datasets used would ideally be readily available, routinely updated, representative of the environmental factors of concern and of an acceptable and comparable quality [ 7, 9].
Since exhaustive validation of both somatic and non-somatic events was not available for the datasets used, we measured the concordance of the somatic predictions with databases known to be enriched for somatic or germline mutations.
Thus, we wish to find a set of values for the filtering parameters that would lead to the lowest measure across the three datasets used here.
Many have considered what leads to this high utilization and the answers provided have depended upon the independent measures available in the datasets used.
While our idea of "large" datasets used to be measured in hundreds of gigabytes, based at least in part on what we could easily store, manipulate, and display in real time, today's science and engineering are producing terabytes and soon even petabytes, both from observation via sensors and as output from numerical simulation.
For each measured dataset, the normalized cross-correlation coefficient ρ was calculated and used in (25).
We summarized main classifiers, performance measures, extracted features and benchmark datasets used in the area.
Table 1 displays means and standard deviations of BMD, bone mineral content (BMC), bone area, area-adjusted BMC (aBMC) and other related measures for each of the three datasets used in this study.
