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Some strain lineages in our MLSA dataset were represented by fewer strains than other lineages, which could have influenced our analysis.

Applying our DNA barcode method to the same dataset, we observed that 50.36% and 47.52% of this dataset were represented by the Firmicutes and the Bacteroidetes phyla, respectively (Table 3).

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After that, the evolution of solutions converges when the near optimal partitioning of the dataset is represented by the fittest solution with the respect of the employed clustering validity index.

In designing the proposed classification method, each class of dataset is represented by a set of sub-graphs (components), and a new extension of the k-associated optimal graph algorithm is introduced in the training phase to combine the smaller components.

To construct the graph, each gene in the dataset is represented by a node.

MicroRNA dataset is represented by i patients (i = 1,..., N) and l miRNAs (l = 1,..., N mi) and gene expression dataset is represented by j patients (j = 1,..., N) and m genes (m = 1,..., N G)).

The dataset was represented by 3012 phylotypes defined at 100% identity (100%-ID phylotypes or distinct sequences).

Each residue in the SCR dataset is represented by 212 features that include sequence and structural information extracted from the homologous alignment and 3D structure (Supplementary Material 5).

Additionally, due to sampling limitations, certain clades in our dataset are represented by species that do not necessarily reflect the full diversity and disparity of their respective clades.

The dataset was represented by 3,011 distinct sequences (100%-ID phylotypes) of ~215 nucleotides and 583 phylotypes defined at ≥97% identity (97%-ID phylotypes).

Each record (or row) in the dataset is represented by a marker whose position depends on its values corresponding to the X and Y axes.

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