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Each bin contains the number of occurrences of scores in the data set (hat {gamma }_{mathrm {b},k}[!n]), n∈ [ 1,Q], which fall within that bin.
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Subtracting the value of N = 10β(m) calculated for m = M2 from that calculated for m = M1, we finally obtain the forecast number (λ iM ) of events in the ith box that fall within the bin M1 ≤ M < M2 during the period from t2 to.
Each K-dimensional bin contains all points pairs ( q i, q j ) ∈ P whose measurements {m k (q i, q j )} fall within the bin ranges.
It then divides genomic coordinates into bins and gives each bin a value corresponding to the mean or maximum signal values which fall within the bin, or assigns value based on the number of 'active regions' which fall within the bin.
which counts the number of orientations that fall within a bin.
This analysis enables us to compare, for each score bin defined by an algorithm, the proportion of target site predictions and the proportion of experimentally validated predictions falling within the bin; particularly, it shows the proportion of experimentally supported targets that overlap with the highest scoring bins.
Differentially-expressed transcripts are detected based on the number of reads that fall within each bin in each sample.
We sample independently from each bin such that the number of genes drawn corresponds to the number of dataset genes that fall in that bin.
For each robustness bin, we determined the number of genotypes of the sample that fall in that bin; and the jackknife estimate gave the error bar on the height of the bin.
For each dataset, we then identified all the trials for which the last decoded time point happened to fall in that bin.
That of Fig. 9c gives the total wave energy flux of the total number of points falling within the bins.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com