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Machine learning methods in theory should be able to approach performance of human raters but they require a large quantity of scored data, proper feature representation, intensive feature engineering, and model selection.
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For analysis of score data from the tightrope test, however, the non-parametric Wilcoxon-Mann Whitney test was used.
It is noteworthy that this result was obtained without using an important resource: the information about the quality of scoring data.
Minimal internal variance of score data between duplicate tissue cores from the same patients was detected in the tissue arrays, showing a high agreement for each protein (r > 0.95 and p < 0.0001, for each protein).
Minimal internal variance of score data between duplicate tissue cores from the same patients was detected in the tissue arrays, showing a high agreement for each protein (r>0.95 and P<0.0001).
Minimal internal variance of score data between duplicate tissue cores from the same patients was detected in TAs, showing a high agreement for each protein (r >0.95 and P<0.0001, for each protein).
This article presents a comparison of scoring methods and data treatments on CERT codes.
For SomaticSniper, examination of the distribution of somatic scores (data not shown) indicates that a cut-off of 20 can reduce false positives without compromising sensitivity.
In places like Florida and Washington, D.C., value-added models have accounted for such factors, in part because of the limitations of using fewer years of test-score data.
Patients were divided into two sets of stable and unstable mood based on a psychiatric evaluation of an initial 6-month period of mood score data.
Analysis of Z-score data revealed a high proportion of infants underweight, stunted or wasted in their first year (Table 3).
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