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In order to determine whether the eyes in this study did not have hidden optic nerve disease, samples of the myelinated nerves were fixed in 4% paraformaldehyde, post-fixed in osmium, embedded in epoxy resin and stained with paraphenylendiamine to detect axon degeneration [66], [67].
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All disease samples were cases of spontaneous ALS, with a mean patient age of 68.2 ± 7.6 years.
In many cancer studies, a gene may be expressed in some but not all of the disease samples, reflecting the complexity of the underlying disease.
Consider a hypothetical case of a marker that is 2-fold different (1 on the log2 scale) in just 10% of disease samples with zero variability and with 90% of disease samples the same as control group.
This contrasts to results reported by others, which may be explained by differences in study methodology, definition of recurrent disease samples and generally small numbers of patients assessed.
A procedure to generate clusters of disease samples from gene expression measurements through the use of RF is described here.
In general, outlier analysis offers a unique and powerful approach for the identification of key pathogenetic genes involved in a subset of disease samples.
Advances in next-generation technologies have led to identification of genome-wide DNA methylations in a large number of disease samples.
The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
A gene showing over-expression in a subgroup of disease samples based on a cut-off threshold is defined as an outlier (Figure 1).
Computer-aided recognition of disease samples and grade patterns [ 10], hence, holds the potential for more accurate, reproducible and automated diagnoses [ 11, 12].
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