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If only the predictions with the top scores, i.e., those comply with both of the first two rules, are considered a positive prediction, the number of predicted TFBS is reduced to 130 while still capturing the same seven true TFBS.
Although the use of these graphs presents some advantages, like reducing the number of classifiers consulted at each prediction, the number of possible graph structures for a given problem is factorial on its number of classes.
To test this prediction, the number of view-invariant representations required during the dual-task method was manipulated by varying whether or not the geons in each task needed to be recognized across a viewpoint change.
In our prediction, the number of beta-sheets ranged from 3 in GmPSL3 and CsPSL1 to 9 in CpPSL1.
In gene function prediction, the number of positive gene-to-function relationships is far smaller than that of negative relationships.
Consistent with theoretical prediction, the number of AuNSs on each JNP decreased with increasing sizes of AuNS.
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Despite high ambiguity in structure prediction the numbers of synonymous substitutions are consistently lower (WMH<1) at paired sites (Table 4).
However, as soon as the prediction specificity is increased by filtering out weak-energy predictions, the number of target sites per thousand nucleotides increases in the transcripts-with-motif.
Both algorithms return the same number of true-positive predictions; the number of false-negative predictions is also equal, leading to identical sSn results.
When compared against the entire ~75k gene model predictions the number of genes unique to G. max is 2,533.
After optimization of the neural network, the predictions were fairly accurate given the relatively small number of structures, especially in the prediction of the number of nitrogen and sulfur atoms of the ligand.
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