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Generally, kinases from the same phylogenetic group as defined by Manning et al. tend to group in the same cluster in our revised tree based on bioactivity profiles.
Therefore, it is interesting to observe that the groups of compounds identified from the target network were, to certain extent, consistent with those obtained by the clustering analysis based on bioactivity profiles.
Furthermore, a complete compound target interaction network, which was drawn for all the compounds identified from the six clusters, produced roughly the same groups of compounds as that obtained by hierarchical clustering analysis based on bioactivity profiles.
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The MUV data sets are based on bioactivity data.
Interestingly, these outliers were grouped together in 2 clusters in an MDS plot based on bioactivity.
However, for some tasks this is too restrictive and other selection criteria could be useful, e.g., based on bioactivity [43].
We hence assessed the number of shared active compounds between each pair of kinases as a measure for SAR similarity and compared this number to the distance based on the bioactivity profiles (shown in Additional file 7: Figure S7).
Among others, chemogenomic approaches have enabled: (i) the prediction of protein targets for new compounds based on the bioactivity profiles of similar compounds, [7 9] (ii) the study of protein similarity on the basis of the similarity of their ligands, [10, 11] and (iii) receptor deorphanization [12].
Alternatively, a compendium approach examining multiple cellular response parameters (e.g., gene expression levels and growth rates) can be used to infer the drug targets of a novel compound based on reference bioactivity profiles of well-characterized drugs [5], [6].
Compounds were hierarchically clustered based on their bioactivity profiles.
Hierarchical clustering of compounds was carried out based on their bioactivity profiles.
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