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A computer program measures the results, determining the quantity or size of the fragments, at each data point, from the level of fluorescence intensity. [1] Samples which contain higher quantities of amplified DNA will have higher corresponding RFU values. [2] [3] An "RFU peak" is a relative maximum point along a graph of the analyzed data.
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
Two criteria to determine the C q are used by different thermocyclers: threshold cycle (C t) is the number of cycles required for the fluorescent signal to cross a given value threshold. Usually, the threshold is set above the baseline, about 10 times the standard deviation of the noise of the baseline, [1] to avoid random effects on the C t ...
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Suppose two values x (1) = 2, x (2) = 4 were sampled from the exponential distribution F(x;λ) = 1 − e −xλ, x ≥ 0 with unknown parameter λ > 0. In order to construct the MSE we have to first find the spacings:
In order to resolve the challenges where it is hard to determine the dual-threshold value empirically, Otsu's method [3] can be used on the non-maximum suppressed gradient magnitude image to generate the high threshold. The low threshold is typically set to 1/2 of the high threshold in this case.
The threshold value to determine when a data point fits a model (t), and the number of inliers (data points fitted to the model within t) required to assert that the model fits well to data (d) are determined based on specific requirements of the application and the dataset, and possibly based on experimental evaluation.