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Given the possible confounding between the potential predictors of mutation status identified above, it is essential that statements about the absolute and relative probabilities – and therefore the predictive effects of factors – be based on an analysis that takes into account all factors simultaneously.
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The hypothesis that mutation status identifies distinct subtypes is further supported by the differences in copy number profiles and age distribution between these patient groups.
The correct mutation status was identified when an Rm/Rwt ratio cut-off >0.7 was used; however, in 68 assays, a cross-reactivity signal was observed.
Predictors of mutation status were identified using multiple logistic regression.
NPM1 mutation status was identified as described by Noguera et al. [ 27].
In 142 patient samples, the NOTCH1 mutation status was identified by sequencing of PCR-amplified products.
Despite this, when PgR was dichotomised as positive or negative, no significant association with mutation status was identified (Table 1).
Mutation status was identified by RFLP, which displayed two, wild-type DNA bands (120 and 95 bp) digested by the TspRI restriction enzyme when the BRAF V600 mutation was absent, and three DNA bands (215, 120, and 95 bp) when the BRAF V600 mutation was present (heterozygote).
Elastic-net analysis of mutation data with MSI status identified 21 genes including TGFBR2, CASP8, and ACVR2A while the analysis of methylation data listed 30 genes such as MLH1, FLVCR2, and EFNA1 (Table 1).
In this study, we stratify breast cancers based on their TP53 mutation status and identify the set of dysregulated tumorigenic pathways and corresponding candidate driver genes using breast cancer gene expression profiles.
In this study, we stratify breast cancers based on their TP53 mutation status and identify the set of dysregulated tumorigenic pathways and their candidate driver genes by using gene expression data sets obtained from tumours.
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