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This cassette could be used to express gene of interest as commonly used gene expression unit.
This study used gene expression as an alternative, because a gene needs to be expressed before the protein is expressed.
We used gene expression data from mouse peripheral blood cell (PBC) samples to identify significantly differentially expressed genes using supervised classification and sparse ANOVA.
We used gene expression data from two predefined subclasses of human HCC to develop and train the prediction methods.
We used gene expression to help visualize the PSB and telencephalic compartments: the pallium can be marked by pax6, emx3 or tbr1 expression, and the subpallium by dlx2 or isl1 (see below for in situ hybridization (ISH)).
In 2006, Philipps et al used gene expression to divide GBMs into 3 groups (i.e., proneural, proliferative and mesenchymal), which are associated with different prognoses (Phillips et al, 2006).
To test their hypothesis, Roux and Robinson-Rechavi used gene expression data from mice and zebrafish.
We previously used gene expression array analysis to distinguish subsets of advanced cancers based on disease outcome.
Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients.
Nevertheless, most analyses have used gene expression profiles to define broad group distinctions, similar to the use of traditional clinical risk factors.
To better understand the relationship between liver CD34+CD146+ and CD34+CD146- subsets and any effects of disease on CD34 development, we used gene expression profiling and computational modeling to compare each subset during ALD and HCV.
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