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Detection of molecular subtypes, for instance by using microarray gene technology, can give new prognostic information which can assist personalized treatment planning.
Demonstrating activation of the CLEAR network can be performed by showing synchronous upregulation of CLEAR network genes by using microarray gene expression profiling (Palmieri et al., 2011).
Multiple technologies, such as cloud computing, parallel systems, and data analysis strategies, have been developed to identify differential gene expressions by using microarray gene expression data [ 6– 10].
By using microarray gene expression analysis, we examined the effect of PROK1 on gene expression in myometrial tissue before the onset of labor at term.
By using microarray gene expression data from mammals [ 47], fruit flies and yeast [ 48], it has recently shown that as gene family size enlarges, there is a general trend for paralogous genes with decreased breadth and increased specificity of expression.
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Furthermore, the polyamine biosynthesis gene AMD1 and the Cyclin-dependent kinase CDK11, both located at 6q21, as well as the tumor suppressor gene SASH1 at 6q24.3 were found to be under-expressed by Suarez-Merino et al. [62] using microarray gene expression analysis.
Our analytic approach improved the previous studies of breast cancer prognosis using microarray gene expression data by jointly analyzing adjuvant-treated patients and untreated patients.
This was accomplished by using microarray analysis to compare gene expression in bone from individuals with end-stage OA disease and individuals without obvious OA (control or OP).
By using microarray analysis, we identified genes that are highly expressed in L2 of the rat GRS at P3 (preceding dendritic bundle formation).
It might be interesting to investigate the CREB-dependent expression changes by using microarray analysis, because unknown genes downstream of CRH-1 might function as regulators for calcium concentration.
In order to investigate the genetic functions and associations between genes and kidney-related diseases, we categorized genes in the human kidney model into HK genes and KS genes according to previous work [ 23] which identified human HK genes and tissue-specific genes by using microarray meta-analysis.
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