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We argue that care must be taken interpreting the gene expression data.
When interpreting the gene expression data from the two xenograft models, it should be kept in mind that gene expression not always represents the actual enzymatic activity.
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Several different methods were used to comprehend and interpret the gene expression responses.
Although it is not purely automatic, this step was where we incorporated prior biological knowledge to interpret the gene expression dataset.
Therefore, in order to interpret the gene expression data reliably and robustly, specificity to coral host genes was first confirmed by using aposymbiotic cDNA and sequence identity (BLAST) in order to avoid any affinities of the primers for symbiont derived cDNA.
To interpret the gene expression data better, we have used GeneBins to provide hierarchical functional classification modelled on KEGG ontology [ 39, 40].
The T-profiler software [ 18] and the YEASTRACT database [ 19] were used to interpret the gene expression profiles at the 24-hr time point by finding consensus motifs upstream of significantly represented groups of genes and associated transcription factors.
The published relapse-free-survival (RFS) times for patients in this trial are used to label each patient as <5 yrs RFS (blue) or >5 yrs RFS (red), allowing the physician to interpret the gene expression patterns in context with the end point of interest.
Although deregulation of the Sprouty genes can indicate a general aspect of the Sprouty status in a given cancer, this needs to be interpreted in relation to the gene expression at the protein level and pertinent functional outcomes.
Without a database to compare chemicals with unknown MOAs, the risk assessor will not be able to interpret the significance of the gene expression responses within the context of characterizing ecological risk, because many gene expression changes are not anchored to adverse effect and risk assessment requires knowledge of the MOA and dose response relationship.
Thus, the consistency degree, which is calculated solely from the training datasets, is definitely valid for the application of interpreting, testing and defining the gene expression profile separability of the given pathological status.
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