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In addition we have evaluated the maximally enriched substructures in the active dataset of small molecules with activity against mir-21.
For our analysis, we use a passive measurement dataset with more than 319.3B connections since February 2012, and an active dataset that contains TLS and SSL scans of the entire IPv4 address space since August 2015.
Table 2 Significantly enriched scaffolds in the active dataset Scaffold_No.
We used substructures which have at least > 1% matches among the active dataset entries.
Alignment of 14 enriched scaffolds (dark green) with top 20 compounds of active dataset.
We also performed an alignment of the 14 enriched scaffolds with top 20 compounds of the active dataset (Figure 4).
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Therefore, as in the case shown by earlier studies [13], SVM has certain capacity for identifying novel active compounds from sparse as well as regular-sized active datasets.
We found that in 32 of 224 (14 %) modules, the average pairwise Pearson correlation of any two genes in the module is < 0.5 in the corresponding active datasets.
For 72% of those module pairs within the same second-order cluster, the average gene pairwise Pearson correlation between modules in the active datasets is less than 0.5 (normalized Percentage Bend correlation approximately < 0.35), and for 30% module pairs the cross-module average gene pairwise Pearson correlation is less than 0.3 (normalized Percentage Bend correlation approximately < 0.19).
From the over 390,000 remaining entries, those with similar physicochemical properties compared to the FXR-active dataset were kept.
In addition, active FXR ligands from the FXR-actives dataset were fitted into the model.
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