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Finally, the Level 1 scaffolds for each dataset were clustered and visualized by Tree Maps.
The Level 1 scaffolds for each dataset were clustered by their fingerprint similarity using the ECFP_6 fingerprints.
All ligands of each dataset were clustered with LigandScout using default settings except for the cluster distance that was adjusted for each NR to obtain balanced clusters.
To perform the LB pharmacophore modeling approach, the ligands of each NRLiSt BDB dataset were clustered using the Pharmacophore RDF-Code similarity.
Genes arising from the syngenic tumor dataset were clustered using the technique described in [13].
All cell lines in CCLE dataset were clustered based on their baseline gene expression.
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Next, the training dataset is clustered by the DBSCAN to select the representative data for the ENNs.
Districts in the dataset are clustered into 3 clusters using CLARA algorithm.
Figure 5 The COX-2 dataset is clustered into 7 clusters.
The denoised dataset was clustered into operational taxonomic units (OTUs) with 97% similarity threshold, using the UCLUST algorithm.
Then, this dataset is clustered ten times with the PM-TS clustering algorithm, K-Medoids clustering algorithm [29], and the spectral clustering algorithm [26].
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