Sentence examples for order detection and from inspiring English sources

In our previous study [ 17], we compared between the networks of each cancer and its corresponding normal PPI, which were obtained by the AIC (akaike information criterion) order detection and Student t-test methods from microarray expression data of patients and normal people, respectively, to get the PPI differential network in order to reveal PPI alternations during the tumorigenesis process.

After the interaction number M i ′ was determined using the AIC order detection and Student's t-test, spurious false-positive PPIs, α ^ i j, in (2) were pruned away, and only significant PPIs that remained were refined as follows: (3) x i n = ∑ j = 1 M i ′ α ^ i j x j n + w i ′ n, i = 1,2, …, M where M i ′ ≤ M i denotes the number of significant PPIs of the PPIN, with the target protein i.

After P values were determined using the AIC order detection and Student's t-test, spurious false positive PPIs α ^ i j in (2) were pruned away and only the significant PPIs that remained were refined as follows: (3) x i (n ) = ∑ j = 1 M i ′ α ^ i j x j (n ) + w i ′ (n ),   i = 1,2 … M, where M i ′ ≤ M i denotes the number of significant PPIs of PPIN, with the target protein i.

One detection module, "Single Order Detection," detects disruptive behaviours by identifying abnormal patterns of every single trading order.

To remove false positive PPIs from each candidate PPIN for different biological conditions, we used both a PPI model and a model order detection method to prune each candidate PPIN using the corresponding microarray data to approach the actual PPIN.

If there is no PPI between proteins i and j or it is pruned away by AIC order detection due to insignificance in the refined PPIN then α ^ i j = 0.

If there is no PPI between protein i and protein j or it is pruned by AIC order detection due to the false positive PPIs in the refined PPIN, then α ^ i j  = 0.

If there was no PPI between proteins i and j or it was pruned away by the AIC order detection due to insignificance in the refined PPIN, then α ^ i j = 0.

Multi-dimensional Optimal Order Detection (MOOD) breaks away from classical limitations employed in high-order methods.

Figure 21 An MDL example; the vertical axis is the probability of order detection.

The minisatellites were named according to their order of detection and the satellites according to conserved internal restriction sites (Table 1).