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The overlap measure indicates the degree of overlapping between two clusters, which can be quantified by computing an inter-cluster overlap [29].
This tendency to keep the bending direction can be quantified by computing the autocorrelation function of the bending angle (Fig. 5).
This difference can be quantified by computing the mean magnitude of the perceptual boundary effect for each group (averaging across absolute values of the perceptual boundary shift for each subject), as shown in Figure 4B.
This similarity can be quantified by computing
Enrichment in a certain GO category can be quantified by computing the probability that the number of non-coding DNA elements in a sample of size n that are associated with a GO category, N GO, is larger than or equal to the observed value m assuming the frequency P GO in all genes.
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The similarity measure between two proteins was quantified by computing the frequencies of a set of functional terms that two proteins share based on semantic similarity measure [46].
Two different yet complementary network properties, i.e., local connectivity and global topological properties are quantified by computing two different types of kernels, i.e., a vector-based kernel and a graph kernel.
The AEC performance is quantified by computing the Echo-Return Loss Enhancement (ERLE) measure [62] text{ERLE}=10log_{10}lefrac{mathcal{al{E} left{ {y_{text{echo}}^{2} k)} right}}{mathcal{E} left{left|y_{text{echo}}(k) - hat y k)right|^{2}right}}right) text{dB}, (50).
The performances of the different models were quantified by computing the average mean square error (AMSE), and the average correlation coefficient (ACC) between the true and estimated visual parameters, defined as ϵ = 1 4 T ∑ r = 1 4 1 σ v r 2 ∑ t = 1 T õ v r t - o v r t 2, (11) ρ = 1 4 T ∑ r = 1 4 ∑ t = 1 T ( o v r t - μ v r ) ( õ v r t - μ ~ v r ) σ v r σ ~ v r, (12).
The overall performance of the ROC analysis was quantified by computing area under the curve (AUC).
Genetic differentiation among populations was quantified by computing pairwise differentiation Φst for mtDNA using ARLEQUIN 3.1 [ 44].
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