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The isotope enrichments in the metabolites were calculated and used to infer activities of specific metabolic enzymes and the associated pathways.
Learning sensible parameters for gene interactions is essential for PARADIGM to infer activities within each sample.
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We originally use a similar method to infer activity changes of transcription factors by integrating microarray data with ChIP-chip data or motif discovery data [44].
To summarize, the general concept is to use topological properties of the global protein interaction network to identify non-local regulation patterns and associate a set of putative targets with each signaling molecule of interest, then assess expression profiles of these targets and infer activity of corresponding regulators.
The hypothesis is that absolute and comparable measurements of gene expression can be used to infer activity of the encoded enzymes.
Previously, we also proposed a Kolmogorov Smirnov test like method to infer activity changes of TFs based on their binding association with sorted expression profiles [ 24, 25].
Here we built a multivariate regression model to infer activity changes of TFs beyond copy number and methylation status variations using TF binding sites as features (see Methods).
In the simplest approach, the model signature is treated as a gene-list and proceeds by clustering the genes across clinical tumour samples to then infer activity scores over the separate clusters [ 2].
A further advantage of CoGAPS over standard class-comparison or clustering algorithms is its ability to infer activity in subsets of genes concurrently affected by the diverse biological processes in each sample type [ 12, 19, 29].
We calculated the LOD score to test the linkage of the RBPs inferred activities at each locus.
Recent studies utilized fluorescence detection of green fluorescent protein-microtubule-associated protein 1 light chain 3 B (GFP-LC3) vesicles [ 13, 14], specific autophagy substrates [ 15], or luciferase-based assays [ 16] for inferring activities.
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