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Open image in new window Fig. 13 Prioritization performances of all algorithms in terms of time.
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Since chromosomal CN alterations represent a key feature of cancer, well-performing GE/CN analysis methods are expected to have a good prioritization performance of known cancer genes.
The 3-order approach-specific datasets corresponding to different methods based on prioritization performance.
The benchmarking pipeline reveals systematic differences in cancer gene prioritization performance of currently available implementations.
The prioritization performance about method comparison on approach-specific datasets (K = 2).
They report that integrating tissue-specific data substantially raises their candidate disease prioritization performance.
The benchmarking pipeline reveals systematic differences in cancer gene prioritization performance of available implementations across five case studies.
The comparison of 12 algorithms with respect to their cancer gene prioritization performance revealed systematic differences across independent data sets, preprocessing scenarios and sample sizes.
The cancer gene prioritization performance of the comparison methods as quantified by the AUC analysis is summarized in Figure 1 (for the ROC curves, see Supplementary Figure S1).
To evaluate the prioritization performance of each tool, we plotted the Receiver Operator Curve for each algorithm using a set of nondamaging variants (drawn randomly from the 1000 genomes project (1KGP) Pilot Phase Altshuler et al., 2010) and a set of disease-causal variants (from HGMD database) (see Supporting Information for details).
These information comprises test cases, test pools, prioritization, and performance evaluation methods.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com