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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
We define the score of a core collection as the number of alleles summed over the m microsatellite loci.
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.
We define the score of a mutation as the total score of the mutant conformation minus the total score of the WT.
Now, we define the score of as We define σ(T1, T2) as the maximum score of a local alignment of T1 and T2.
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The following defines the score of a segment matching to a query.
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.
More specifically, FATCAT defines the score of the alignment that ends with P i as follows: where C P j → P i ) is the penalty incurred when connecting P i to the alignment that ends with P j and it is similar to the penalty function used in the general chaining and W(P i ) is the score of the AFP itself.
We further define the score P w of a given word as P w = μ w /∑ w ' μ w '.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com