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Figures 1 and 2 show the first two principal components of the transformed breed-specific kinship matrices to visualize genetic distances between animals.
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A neighbour-joining tree was constructed from a pairwise distance (= 1− R) matrix to visualize the relationships based on accessory genome content.
Four cluster centers from the connectivity matrix are shown for example clusters in Figure 3. Clusters can be mapped from QAA-space onto the connectivity matrix to visualize accessibility between substates.
We next use PPI functional intensity matrix to visualize the functional relationship between the two interacting proteins in PPIs. Figure 3 shows that the H37Rv STRING PPI dataset (with score ≥ 770) has strong intensity at the diagonal of its PPI functional intensity matrix.
Therefore, we used dot matrix plots to visualize repetitive DNA in the accD coding region of M. truncatula ecotypes (Fig. 4E).
Confusion matrices were used to visualize annotation performance for individual semantic labels.
This matrix helps us to visualize the orthogonalization process, and thus to draw a diagram of the four-channel prediction filter structure under consideration as in Figure3.
This matrix helps us to visualize the orthogonalization process, and thus to draw a diagram of the four channel DFE structure under consideration as in Figure 5.
Dendrograms were constructed to show the degree of relatedness among strains according to a previously described algorithm [20] and similarity matrixes were generated to visualize the relatedness between the banding patterns of all isolates.
Principle coordinate (PCO) analyses based on Gower's centered matrix was used to visualize the patterns of differences in the multivariate chemical structure among groups [ 48- 50].
We projected the MD trajectory onto the first principal component, corresponding to the largest eigenvector, of the covariance matrix in order to visualize the extreme structures and the major fluctuations of the correlated motions.
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