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We measure the specificity of the predicted blocks by computing the proportion of genes from all reported alignments or clusters that are also located in simulated blocks.

To further assess the quality of predicted clusters, we also measure the specificity and sensitivity of predicted complexes with respect to the set of benchmark complexes.

Further, we utilized these RNA-seq datasets to measure the specificity and sensitivity of each gene quantification method examined in this study.

In this study, we compare the known binding sites of the DCC with oligonucleotide profiles that measure the specificity of the sequences of the D. melanogaster X chromosome.

Therefore, we tried to measure the specificity of binary complexes formed by the complete functional core of PPP1R15 631–701 and PP1.

This could be attributed to a number of factors including the insensitivity of the chosen outcome measure, the specificity of the computer exercises and the number of repetitions carried out.

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In contrast, the FSim method uses LC values to measure the specificities of GO terms, which only depend on the GO database.

The F-measure is a measure combining the specificity and sensitivity measures.

The information theory approach for studying the transcriptome has the advantage of allowing an estimation of the degree of global gene specificity, Si, of each gene studied, as well as its target specificity, TSij, a parameter that measures the specificity of a given gene for a selected transcriptome (see Mathematical Addendum in Supporting Text S1).

We measured the specificity and sensitivity of GHT and compared it to the ANN classifiers.

measures the specificity of a shared GO term, which we will later use to weigh the contribution of the term.

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