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It detects the nearest neighbors of each elements in a training dataset (S) via a similarity measure, expressed by a distance function (i.e., Euclidean, Manhattan, Minkowski).
This procedure resulted in a matrix where each of the 40 clusters is a sample, each gene is a variable, and each elements in the matrix have the value 0 or 1.
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The elemental mapping (Figs. 1e h) illustrates the distribution of each element in the nanoparticles.
Let's consider each element in turn.
We will address each element in turn.
Calculate the group number for each element in the array.
Each element in this show seems like a potential point of departure for further expansion.
That makes it possible to identify each element in other ways.
In the following sub-sections, we examine each element in greater detail.
One possibility is to examine the contents of each element in turn.
To map a function over a sequence, we do not just select a particular element, but each element in turn.
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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