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The proposed algorithm which focuses on these two dimensions has been implemented and tested using a Cluster tree based key management scheme and has been found to produce promising results.
This was done using a cluster tree as suggested by Trochim.
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Studies focusing on CST using VNTRs or SNPs have mainly described differences in bacteria genotypes between the two host species, and some have reconstructed the bacteria phylogeny using a clustering analysis, a phylogenetic tree or a network approach (see Table 1 for examples on identifying CST using genetic markers).
The 1,042 genes occurring simultaneously in the six comparisons above (a-f) were screened out, and the comparison ratio values of these genes were used to build a cluster tree.
Arrange a row of trees along a mantle, or use a cluster of varying-sized trees to make a beautiful and festive centerpiece.
Through the computation of these two analyses, we created a cluster tree and scatter diagram using PCI and PCII (see Figure S2 in Supporting Information File S2).
A cluster tree arrangement has been used to group similar patches in [6].
The relatedness of the bacterial communities in the five dietary treatments was measured using a hierarchical tree cluster analysis on the proportion of individuals in each treatment possessing each bacterial tRF, where distances are Euclidean and complete linkages were used to determine relatedness [56].
To facilitate this step, we wrote a Perl script that uses the cluster tree as input and retrieves the sequences included in each clade representing an OG based on a cutoff branch length that was empirically chosen to meet our OG criterion.
The minimum number of biomarkers and maximum sample size to reproduce the same clusters were determined using the unsupervised cluster tree.
Specifically, we use the cluster tree implementation from bx.python (https://bitbucket.org/james_taylor/bx-python/) to find the set of all "regulatory sites".
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