Sentence examples for by comparing matrices of from inspiring English sources

It is achieved by comparing matrices of pairwise genetic and phenotypic similarity across unrelated individuals (Yang et al. 2011).

To assess the level of genetic differentiation of mites within and between bat colonies, single-level and hierarchical AMOVAs were performed in Arlequin 3.5 [ 43]. Isolation by distance was measured by comparing matrices of FST / (1- FST) and log transformed geographic distance using Mantel tests (10,000 permutations) in FSTAT 2.9.3 [ 39].

This was estimated within each group, by comparing the matrices of distance based on each partition of the mandible (Table 3) and by RV coefficients (Table 4).

The P value is calculated using binomial distribution probability by comparing the matrix match of the promoter regions of differentially expressed genes with the promoters from random mouse genes sets (-2kb for all the promoters).

Candidate proteins to be considered for TOL and/or barcoding studies were assessed from 33 fungal proteomes by comparing (i) distance matrices of each individual orthologous protein (KOGs) matrix, (ii) to compare these with that of a well supported guide tree [ 14], and (iii) analyze for their phylogenetic signal.

Matrix effect was evaluated by comparing extracts of spiked matrix with extracts of spiked blank matrix using water as blank matrix sample.

By comparing the confusion matrices of Bayes and OVFDT, as shown in Figures 12 and 13, respectively, one can observe the reason behind the shortcoming of Bayes prediction.

The excitation mechanism of Pr3+ ions was further explored by comparing two matrices.

The effect of the presence of matrix on the calibration function was calculated by comparing the slopes of matrix-matched and matrix-free curves.

In this research, the structure's health or the level of damage is monitored by identifying changes in structural parameters, by comparing the stiffness matrix of a structure with the undamaged model matrix.

Random Matrix Theory estimates the number of significant components of a data covariance matrix by comparing the statistics of the observed eigenvalues obtained from PCA with those obtained from a random matrix.