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In both cases, their ability to find novel miRNAs is dependent on how representative positive and negative examples turn out to be.
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(The first of these two examples turned out to be less "obviously true" than first appeared, but that's another story).
Linear support vector machines were implemented whenever the examples turned out to be linearly separable.
The only problem was that their examples turned out to be pro-Assad graffiti.
An organisation may, for example, turn out to have a building code violation or financial irregularity.
(His "miniature royal portraits", for example, turn out to be postage stamps with the king's head on them).
Cash transfers, for example, turn out to be used wisely by recipients, and spur entrepreneurialism rather than trapping people in dependency.
Inspired by the patterns she sees in everyday things, Hasking works with single and multiple images to create a repeating pattern that from a distance reads as texture, but on closer inspection reveals more – the regular grids of letters in the word search papers, for example, turn out to be full of concealed words (many of them Hasking family jokes).
They may for example turn out to be costly in terms of level of employment; the share of temporary employment in firms' employment may be suboptimal.
A. Cohen, for example, turns out to be the writer Arthur Cohen.
The Bombay example turns out to be something of an outlier, however.
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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