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Several genomic variants had high frequencies in populations e/o cohort of samples with particular ancestries and such variants could erroneously appear to be related to disease.
The method described here can be useful to identify samples with particular IL-28B genotypes for functional studies.
The remaining biomarkers: Heregulin, TUNEL and Survivin, were represented in 55% or fewer samples, with particular scarcity in EGF104911 (<20%).
Here the number of observed samples with particular species was compared to what would have been expected if the species being considered were randomly distributed in samples at the frequency observed for each species on its own.
We have also described the substantial complexity of the genetic analysis of the IL-28B locus, and we have presented a new approach to identifying samples with particular IL-28B genotypes for functional studies.
The observed differences among the myeloma cell lines highlight the importance of exploring the effects of these agents in primary samples, with particular emphasis on different cytogenetic subtypes as well as production/secretion of intact vs light chain-only MP.
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Here we consider L. stenodactylum populations within the Pilbara based on much larger and more detailed sampling, with particular reference to testing the hypothesis that ancient surface geology, and the habitats they determine, predicts phylogenetic structure in these rock dependent animals.
The primary limitation is that the ability to perform any particular association study relies on the availability of samples with the particular phenotype or drug exposure of interest.
The significance of each particular profile was then examined by comparing the number of observed samples with that particular profile to what would have been expected to have occurred if the 8 species were randomly distributed in samples at the frequency observed for each species on its own (i.e. each species presence or absence was included in the estimation of what would be expected).
As the importance of samples and data has grown in research, and as genomics research attempts to analyse ever expanding data sets, biobanks are becoming larger and more prospective – collecting samples with no particular research purpose in mind (Expert Group on Dealing with Ethical and Regulatory Challenges of International Biobank Research (2013)).
Not only can samples with that particular gene be detected, but also they can be quantified based on colour intensity.
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