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The clustering analysis based on the Bray-Curtis metrics used the complete-linkage clustering algorithm, which seeks to maximize distance between clusters by calculating the proximity between clusters based on the two most distant objects in each cluster.
The biggest downside of k-means clustering algorithm is the random cluster ordering on the output; however, this information can be indirectly accessed by looking at the average distance between clusters (based on the supplied metric) as well as the number of points in the cluster.
gingivalis HtpG p18 between clusters based on clinical measures, but no differences in the response to the whole bacterium.
Despite the clear distinction between clusters based on nucleotides, no such distinction is found when comparing amino acid differences.
Since the latter method does not readily provide clusters, we used that measure with a K-means-like algorithm (with K = 15, and moving genes between clusters based on average distance).
Future analysis could reveal the potential of assigning transition probabilities between clusters based on physiology, which combined with knowledge of the likelihood of death in each state suggests potential methods of steering the physiology away from clusters with high mortality towards clusters associated with safety.
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Despite this, we noticed an interesting apparent correlation between clustering based on global SH2 profiles and K-RAS mutation status.
The similarity between clustering based on biophysical or chemical properties is obvious, which allows prediction of biophysical properties with a considerable certainty.
We also estimate divergence time between the Northern and Southern lineage and between population clusters based on sequence divergence in the mitochondrial Cyt B gene.
There was also a significant difference between the clusters based on age, as the patients arriving from Miami-Dade cluster were older (16 21 years old), p < 0.001 and those arriving from outside Miami-Dade were younger (mean age 6.8 years, p < 0.001).
Consequently, it is possible to obtain a notion of distance between two clusters based on Granger causality, i.e., a continuous measure (distance in terms of Granger causality) instead of a discrete measure (presence or absence of an edge).
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