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This result suggests that a sub-optimal design of a qPCR validation study can provide a false positive increment in the estimation of the number of q-PCR-confirmed specific ChIP-seq loci and thus it can erroneously predict a larger specificity value than it can be deduced from the whole ChIP-seq dataset.
The worst specificity value is recorded for SVM (79 %).
The efficacy of the proposed system corresponding to the above-mentioned performance metrics has been illustrated in Fig. 4. It is clearly visible from the figure that the system is capable of detecting fraudulent calls efficiently by keeping the Specificity value high (i.e., low false alarm rate).
In contrast, the same number of false negatives will produce a Specificity value of 0.999.
We calculated the specificity value (see Methods) to measure how frequently each domain occurs in UPR proteins relative to non-UPR proteins.
Therefore, attempt was made to adjust the threshold values of BPB-PPMS in order to obtain equivalent sensitivity or specificity value of MeMo.
This Specificity value does not express the real impact of this number of selected false negatives, which should be more comparable to the impact of false positive selections.
Performance comparisons were performed at equivalent sensitivity or specificity value as a result of the absence of threshold choice on MeMo server.
As a result, a significant number of false negative selections will produce only a small deviation from 1 in Specificity value.
Using the 34feature set, the best performance was obtained with a sensitivity value of 91.1 and a specificity value of 88.8.
Thus, uPAR and C4.4A showed a specificity value of 1.00.
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