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From these matrices, we calculate the joint frequency p12 (x2, x2) of the majority nucleotide at positions (x1, x2).
To detect the most significant correlations between parameters present in all correlation matrices, we calculate an averaged matrix – which we call the mean correlation matrix – whose entries are the mean values of the corresponding correlation coefficients in the individual correlation matrices.
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On the basis of contiguity matrices, we calculated concentric rings of administrative districts to illustrate the contextual radius that impacts young adults' vocational training chances.
From the 98 matrices we calculated the range of lambda (λ, intrinsic rate of population growth).
Using these sub-alignments and the corresponding SCPE Q c matrices, we calculated the maximum likelihood using PAML.
For each ranked gene under different weighting score matrices, we calculated the q-value of each SNP in the gene from the COGA GWAS data.
For Hamming distance matrices we calculated binary vectors of mutations by denoting the presence (1) or absence (0) of variants in the VCF4 files generated by GATK.
Given the full set of 522 TRANSFAC mammalian matrices, we calculated the p-value for any given matrix pair MiMj being present in greater proportions in class (C1 and C3) promoters as opposed to class C4.
To measure the sparseness of a matrix, we calculate its 'overall sparsity', which is defined as the percentage of nonzero elements in that matrix.
To measure the column-wise group sparseness of the eigenphone matrix, we calculate its 'column sparsity', which is defined as the percentage of nonzero columns in that matrix.
After building this null adjacency matrix, we calculate the RCA for each cell, applying the pre-process step of our methodology.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com