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Instead, we have used an empirical error analysis to evaluate the confidence that can be placed in each individual model given its supporting microarray data, thus reducing prior bias and avoiding circular reasoning when genes are subsequently compared by profile neighboring.
The RT-PCR results correlate well with the microarray data, thus validating our microarray data.
However, this method was designed for two-color microarray data, thus a non-trivial data transformation on input data was required.
However, ERAP1 transcript was not noticed in the microarray data, thus it remains to be determined whether ERAP1 is functional in osteoblast proliferation and differentiation.
The microarray data thus suggest that a variety of genes and their protein products contribute to acquired drug resistance in breast tumour cells.
We also reasoned the transcriptome dataset has deeper depth than previous microarray data, thus we employed RT-PCR and used the germ cell mutants fem-1, fem-3 and fog-2 to screen for novel sperm specific/enriched genes.
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Available microarray data is thus typically not exploited beyond the scope of the original experiment.
Since the microarray data presented thus far represents treatment of a single population of PBMCs from a commercial source (Lonza, Inc. [identified as PBMC set I]), we wanted to confirm that similar gene modulation would occur in fresh human PBMCs.
With gene interactions being a basis for the very active field of regulatory network construction [31], [32], our method can give researchers the ability to extract potentially disease-related gene sets and related genes from microarray data, and thus is helpful to delineate the sophisticated knowledge of relevant molecular pathways of disease pathogenesis.
Methods to estimate RNA quality directly from microarray data are thus required.
To strengthen confidence in our 180-gene model, we tested an independently derived set of NSCLC cell line microarray data that thus far is unpublished (Girard, GEO # GSE4824).
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