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A distance matrix that was clustered using the "average" linkage method (using the R hclust function).

The active dataset containing 883 compounds was clustered using the LibMCS algorithm which generated a total of 1151 hierarchical scaffolds/substructures spanning up to 6 levels.

For the same purpose as in SCA, this matrix was clustered using the two-dimensional hierarchical clustering method (Figure 5).

The set of proteins was clustered using the BLASTclust algorithm [60] removing redundant structures sharing more than 90% sequence similarity over 90% of the sequence length.

Cluster analysis was performed by pairwise calculation of the Pearson correlation; the similarity matrix was clustered using the UPGMA algorithm with optimisation: 0%, position tolerance: 1%; uncertain bands were ignored.

The expression data was clustered using the self-organizing map algorithm as follows.

The cleaned reads were clustered using the wcd EST clustering tool with an error threshold value of 5 [ 128].

The processed reads were clustered using the MIRA v2.9.26x3 [ 27] assembler with the "denovo, EST, normal, 454" parameters.

Ligand-receptor complex structures were clustered (using the procedure described for quinine).

SSMS and CNVs are clustered using the non-parametric Dirichlet process prior.

The numerous models thus obtained are clustered using the gap metric as the distance measure and representative models are selected.