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A binary matrix was used to compute similarities among the Ganoderma strains (Table 3).
The primary binary matrix was used for producing similarity data using Jaccard's similarity coefficient [ 15] and computed using UPGMA.
The binary matrix was used under the Hardy-Weinberg equilibrium to calculate the percentage of polymorphism (PPB), Nei's gene diversity (H e ), the observed number of alleles per locus (A o ), the effective number of alleles per locus (A e ), Shannon's information index (I), total gene diversity, the level of gene flow using POPGENE version 1.31 [ 60].
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This binary matrix is used to represent the occupied/vacant situation of the licensed band.
For this purpose, the (n × m) binary matrix is used which n indicates the number of distribution centers and m indicates the number of demand points.
For this purpose, the (n × m) binary matrix is used in which n indicates the number of distribution centers and m indicates the number of demand points.
These binary matrices were used for parsimony tree construction.
A binary neighbor matrix was used to account for spatial autocorrelation.
The binary data matrix was used to compute pair-wise similarity coefficients [ 58], and the similarity matrices obtained were utilized to construct a UPGMA-based dendrogram [ 58].
Binary logistic regression analysis with generalized estimating equations (GEE) with an exchangeable working correlation matrix was used.
Such a binary presence absence matrix is used to represent numerous other biological data including restriction sites (Templeton 1983; Nei and Tajima 1985; Felsenstein 1992), indels (Simmons and Ochoterena 2000), introns (Csuros 2006; Carmel et al. 2007), and morphological characters (reviewed in Ronquist 2004).
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