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Parrington lists four types of evidence that ENCODE used to "assess function" (the correct term would be "identify candidate functional elements").
So far, we have seen that as far as functionality is concerned, ENCODE used the wrong definition wrongly.
We note that ENCODE used almost exclusively pluripotent stem cells and cancer cells, which are known as transcriptionally permissive environments.
However much of the specific data generated in ENCODE used pecific immunoprecipitation using antibodies generated against a series of transcription factors.
ENCODE used methodologies encouraging biased errors in favor of inflating estimates of functionality, it consistently and excessively favored sensitivity over specificity, and it paid unwarranted attention to statistical significance, rather than to the magnitude of the effect.
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Specifically, did ENCODE use the wrong definition wrongly in a consistent manner?
We have already seen that ENCODE uses an evolution-free definition of "functionality".
The first dimension concerns the specific encoding used to convert a Bayesian network into a CNF.
Additionally, the molecular encoding used to encode the molecules should result in a sparse feature vector to reduce the dimensionality d.
We now propose an alternative solution to Slepian-Wolf encoding used by cooperative relays in Section 3.1.
A first issue concerns the nature of the neural encoding used by the motor cortex.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com