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We used model coefficients to develop a risk score for detection of advanced colorectal neoplasia.
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Probes were then scored for detection of expression in each experiment.
The background distribution of C3 Scores for detection and identification of influenza viruses was estimated from RPM-Flu assays of 14 different blank samples (water or transport media negative controls).
As far as the relationship between the two types of task-specific information is concerned, the comparison of the scores for detection and individuation showed a weak albeit significant correlation both within the training set (r = 0.25, p<0.001) and within the test set (r = 0.23, p<0.001).
Two-hundred random nuclei were scored for detection of aberrations.
The decision maker fuses these two scores with the Bosaris toolkit, produces the final confidence score for each detection, and ascertains reliable detections.
Next, a search of the given term within the word mesh produces the term detections and outputs a posterior probability as score for each detection.
We reported the detection frequency for individual dimethyl metabolites, not a composite score for the detection of multiple dimethyl metabolites.
This procedure results in a new score for each detection, which is then used to output the final detections of the Fusion (SGMM) + Posteriorgram system.
Wang and Metze [74] focused on score normalization and proposed a term-specific threshold that uses the confidence scores assigned to all the detections of the given term to compute the final score for each detection.
Finally, Bosaris toolkit [129] was used to construct a fusion scheme based on logistic regression; this procedure results in a new score for each detection, which is used by the decision maker of this system to output the final detections.
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CEO of Professional Science Editing for Scientists @ prosciediting.com