Analysis of cell type-resolution expression profiling datasets is moving beyond cataloging gene expression patterns to reveal novel biological insights.
Computational prediction of drug-disease interactions using gene expression profiling datasets and biological networks is a new direction in drug repositioning that has gained increasing interest.
To examine the relationship between USP33 expression and the clinical outcome, we analyzed association between USP33 expression and patient survival by examining publically available microarray profiling datasets for lung cancer.
When different expression profiling datasets were linked together, publicly available datasets were first filtered, normalized and centered as in the original study.
Integrated analysis of gene-expression and ChIP-Seq profiling datasets suggested a role for Gata2 downregulation during the development of AML in our mouse model system.
This is demonstrated by the tight correlation between two large independent expression profiling datasets (Fig. S6), thus yielding a consistent psoriasis-specific pattern of global gene dysregulation.
We compared the miRNA target predictions with the TargetScan software [18] for these miRNAs to the genes down-regulated in our expression profiling datasets.
For Nfib-KO, we used the mRNA expression profiling datasets published in [ 6].
From this, we assembled 14 profiling datasets, denoted here as dataset views.
Two microarray-based gene expression profiling datasets are downloaded from the NCBI GEO database [ 13].
Five different gene expression profiling datasets on breast cancers were analyzed in this study.
