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We evaluated the CI performance using simulated and published microbial typing data sets.
One of the latter methods is the Wallace coefficient (W), which has recently been successfully applied to the analysis of microbial typing data [6].
Here we derive an analytical expression for the calculation of a CI for W and evaluate its performance in simulated and microbial typing data sets.
The distribution of cluster sizes normally found in microbial typing data, as well as in other biological data, is closer to a skewed scenario than to the uniform distribution.
Other methods offer an asymmetric view of concordance, in which the agreement of clustering A with clustering B may be different from the agreement of B with A. An example of this type of measure is the Wallace coefficient (W), which has been applied to the analysis of microbial typing data [3], [6] [8].
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These data could be used for studying species evolution, microbial typing in epidemics, and they are also helpful to discover pathogenic mechanism.
Microbial Typing by Machine Learned DNA Melt Signatures.
This index was proposed as a measure of the discriminatory power of microbial typing schemes.
microbial type culture and collection.
However, this result relies on the assumption that all microbial types have the same carrying capacity.
In 2001, Suneel Kateriya in the Hegemann group identified novel DNA sequences that encoded for large microbial-type rhodopsins in a cDNA data bank from Chlamydomonas.
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microbial sequence data
microbial growth data
microbial research data
microbial metatranscriptome data
microbial survey data
microbial sequencing data
microbial survival data
microbial typing methods
microbial community data
microbial composition data
microbial abundance data
microbial typing studies
microbial susceptibility data
microbial metagenome data
microbial genome data
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