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Mislabeled negative samples could be unknown TSS, which is shown to potentially have a significant effect on the auPRC.
The use of the auPRC should be considered with caution as an evaluation measure of TSS finders since the PRC has a demonstrated sensitivity.
For example, the removal of the 100 negative samples with the highest network scores results in a change of the auPRC from 24.08% to 30.44% and 54.64% to 65.5% for chunk sizes of 50 and 500 respectively.
Each of the four protocols assigns the highest auPRC to ARTS.
The graphs of Eponine and DragonPF indicate that auPRC for those programs may be underestimated.
The area under the auPRC is calculated using the trapezoid method on all precision recall pairs for each algorithm.
In order to evaluate the results, we averaged auPRC values for the minority (positive) class across the ten folds for each organism.
While the trends are generally maintained for individual organisms, we report averages of auPRC values over the five organisms, for easier interpretation.
Even without the use of the tissue ontology, independent SVMs perform reasonably well for easy problems such as discriminating blood samples, and so the improvement of our approach is relatively small (AUPRC of 0.9072 for individual SVMs versus AUPRC of 0.9823 for URS A.
However, using Bowtie 2 alignments, SAMtools performed better than any of the individual variant callers (AUPRC: 0.85), and UnifiedGenotyper proved to be the worst (AUPRC: 0.8).
Additionally, we also computed the AUPRC for VariantMetaCaller and BAYSIC for each chromosome, and found that the AUPRC for VariantMetaCaller was higher than that for BAYSIC in most cases regardless of the type of the variant or the aligner and the differences were strongly statistically significant (Table 3).
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