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Once a score s g (t) is chosen to assess the significance of each transcription factor in the target-gene-specific regression model (1), we can combine them across all target genes by defining the score of a candidate regulation (t, g ) ∈ E as s(t g)= s g (t), and rank all candidate regulations by decreasing score for GRN inference.
Basically it can be done by extending the definition of diagonal to not constrain the lengths of the two segments to be equal and by defining the score of an "extended diagonal" as the alignment score (penalizing gaps) of its two segments.
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GenRev is particularly designed for general use so that users have the flexibility to choose a reference network and define the score of genes.
If K is the factor by which we penalize each base error, we define the score of
The following defines the score of a segment matching to a query.
Now, we define the score of as We define σ(T1, T2) as the maximum score of a local alignment of T1 and T2.
We define the score of a core collection as the number of alleles summed over the m microsatellite loci.
We define the score of a mutation as the total score of the mutant conformation minus the total score of the WT.
Conditional independence allows us to define the score of a given tiling to be the product of the scores of the individual tiles it is composed of.
To consider the differentiating power of a set of markers consisting of k markers, we first define the score of the marker set.
We now define the score of an alignment with structural alterations A s as Here S m denotes the alignment score of each t i to f(t i )—which is higher if t i = f(t i ).
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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