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We used gene expression profiles derived from primary human fibroblasts expressing a variety of viral oncogenes, and applied both GMIT and PANDA to build viral oncogene-associated regulatory networks.
Second, we used gene expression profiles from two different studies.
We also used gene expression profiles of 22 cell lines from Maupin et al.[ 24] and 19 from Collisson et al.[ 25].
To identify markers that aid in differentiating ccRCC from chRCC, we used gene expression profiles to identify candidate markers that correlate with histology.
We used gene expression profiles from The Cancer Genome Atlas (TCGA; available at http://cancergenome.nih.gov/) for classification of ovarian phenotypes based on ovarian cancer tissues.
We used gene expression profiles of pre-implantation mouse embryos at the single cell resolution to visualize the Waddington landscape of the early embryogenesis.
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We used gene expression profiling to identify genes that were co-expressed with genes whose transcripts encode the protein targets of commonly used chemotherapeutic agents.
We and others have used gene expression profiling to identify gene signatures predictive for outcome in ccRCC.
Previous studies have used gene expression profiling to determine the cellular pathway targeted by antimicrobial agents [65], [66].
We then used gene expression profiling to identify the genes whose expression levels were significantly different in the NF versus AF.
In this study we have used gene expression profiling to identify a group of genes that are induced by etsrp OE during early development.
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