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To identify transcripts that responded to the phenanthrene exposure, we used gene expression microarray analysis.
Recently, we used gene expression microarray technology to determine age-related changes of the vascular transcriptome in mice.
We used gene expression microarray to compare the expression profiles of late L4/young-adult stage hlh-25 (ok1710 ) animals to profiles of age-matched, wild-type animals.
To construct a gene co-expression network, we used gene expression microarray data from [ 36], which contains the yeast gene expression data in response to a variety of environmental changes.
For this purpose, we used gene expression microarray data from mouse embryonic fibroblasts in which one of the NFI factors (termed NFI-C) was knocked out by insertional mutagenesis [ 30].
A recent study by Gonzalez-Malerva and colleagues used gene expression microarray analysis, and a functional genomic approach of overexpressing >500 full-length human kinase cDNAs, to study resistance in a series of MCF-7 cells with differing tamoxifen responses [ 14].
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Second, three groups have used gene expression microarrays to identify genes differentially expressed between wing and haltere, either by directly comparing each tissue or comparing normal wing discs with those misexpressing Ubx [10], [11], [42].
We used gene expression microarrays to identify pathways induced by coculture in premalignant cells (MCF10DCIS) compared with normal and benign cells (MCF10A and MCF10AT1).
A recent study used gene expression microarrays to identify differences at the transcription level in bone marrow cells between normal volunteers and FA patients [ 64].
We then used gene expression microarrays to gain insight on the molecular pathways that sustain the PS tumor initiating cell (TIC) phenotype.
We used gene expression microarrays and cell culture replication for technical and experimental validation, respectively, of our initial results from this new technology.
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