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If all the conditions were equally set, the domain-based PON methods and the DCN methods should produce exactly the same results for protein function prediction.
Indeed, there are circumstances in which both methods produce very similar results, but there are also situation that both methods should produce different outcomes.
A good normalization method coupled with gene ranking methods should produce good ranked gene lists where true DEGs can easily be detected as top-ranked and non-DEGs are bottom-ranked, when all genes are ranked according to the degree of DE.
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Nonetheless, the method should produce unitless indicators that can be employed to judge the relative vulnerabilities of diverse systems to multiple stresses and to their potential interactions.
This method should produce unbiased results even with highly variable microsatellite markers.
A good method should produce high AUC values for real experimental datasets [ 1].
Apart from the above discussion, a good method should produce high AUC values for real experimental datasets.
A good method should produce high POG values, i.e., those indicating reproducibility as well as high AUC ones, i.e., those for sensitivity and specificity.
As radio telemetry was not feasible for our study we identified a scan sampling technique used for monitoring abundance of wild birds [ 35, 36] as a method that should produce a qualitatively similar measure of overlap as that measured by UDOI and that could be readily adapted for our study.
However, both methods should ultimately produce a comparable finding.
Equally important, these methods should not produce excessive false-positive findings.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com