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From each bacterium, we selected the genes with PI >=65% and grouped all the putative orthologs together using the COG method [ 46].
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However, no comparison using the CoG and the CoP has been done to the best of the authors' knowledge.
Using the COG classification more than 70 genes were identified to encode for transcriptional regulators.
Additional functional classification was conducted using the COG (http://www.ncbi.nlm.nih.gov/COG/) and KEGG pathway (http://www.genome.jp/kegg) databases.
We stressed the benefits of using the "COG groups" format in the alignment of many networks.
Predicted proteins were functionally categorized using the COGs database [ 40], and COGs categories were compared among the genomes of GP72, Pf-5, M18, and A1501.
The number of proteins annotated using the COGs database was slightly less than RefSeq.
The COG method is adopted since the membership functions used are simple triangular and trapezoidal shapes with low computational complexity and can be expressed as [1]: Z_{{text{COG}}} = frac{int_{z} mu z z dz}{int_{z} mu z dz} (1).
We first used the COG-04 dataset (see Methods) to compare the the extended Psi-square algorithm with our hierarchical clustering approach that used the same data [15].
First, a coarse-grained classification was obtained by assigning predicted genes to the NCBI Clusters of Orthologous Groups of proteins (COGs) using the COGNITOR method [35].
Step 8: Defuzzify w ˜ i by using the center of gravity (COG) method and rank the decision alternatives based on their normalized crisp overall scores w i. w i * = ∫ a c x μ w ˜ i x dx ∫ a c μ w ˜ i x dx (19) w i = w i * ∑ i = 1 m w i * (20).
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