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The frequency rule is obtained by analysing the uncertainty of the wrapped phase caused by intensity noise.
The inclusion of read pairs with more than one mismatch in the alignment can be considered but has a risk of falsely predicted SVs due to mapping errors, requiring a revalidation of our proposed SV size deviation versus the observed frequency rule.
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Furthermore, the independence from frequency rules out visual word familiarity as an explanation.
Euclidean distance of a 100%-frequency rule indicates the separation extent between the HCV+ and HCV- samples.
Similarly in this work, we also define a 100%-frequency rule to differentiate normal samples from cancer samples.
For each 100%-frequency rule containing 2 or 3 miRNAs, we detected target mRNAs of these miRNAs.
Similarly in this work, we also define the 100%-frequency rule to differentiate negative samples from positive samples.
Every 100%-frequency rule with two or three miRNAs can separate the cancer samples clearly from the normal samples in 2D or 3D spaces.
If all of the training samples can be correctly classified by a rule in one of these trees, then this rule is a 100%-frequency rule.
If every positive sample's expression profile satisfies (falls into) the k specific expression ranges, but none of the negative sample profiles satisfies, then we say it is a 100%-frequency rule to differentiate the positive samples from the negative samples.
If every cancer sample's expression profile satisfies (falls into) the two specific expression ranges, but none of the normal sample profiles satisfies, then we say it is a 100%-frequency rule to differentiate the cancer samples from the normal samples.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com