Sentence examples for multiple expression datasets from inspiring English sources

These observations underscore the value of integrating multiple expression datasets in order to derive widely reproducible signatures.

Multiple expression datasets from many brain and peripheral tissues of inbred and RI strains were used in this analysis (see Text S1 for additional methods).

The HapMap lymphoblastoid cell line data consist of multiple expression datasets and cover several continental-level populations.

Most of the existing approaches for mining multiple expression datasets represent the datasets as gene coexpression networks and mine these networks for interesting gene sets (modules) [ 4, 5].

An important contribution to the comprehension of lncRNAs biology in cancer could be obtained through the integrated analysis of multiple expression datasets.

In the context of integrative analysis of expression data from clinical studies, there are two general scenarios: multiple expression datasets collected for the same disease or multiple metadata collected for a single dataset.

In this study, we identified the RNA splicing factor SRRM2 (or SRm300), serine/arginine (SR) repetitive matrix 2 as differentially expressed in multiple gene expression datasets.

In a seminal study by Lamb et al. [2], a set of genes induced by cyclin D1 in an in vitro model were found to be co-expressed as a group with cyclin D1 mRNA in multiple expression profile datasets of human tumors of various types.

We have demonstrated a simple approach to leveraging multiple gene expression datasets to increase the robustness of inferred links between genes, by focusing on links supported by multiple gene expression datasets.

Further, interrogation of multiple expression array datasets identified Endo180 as one of the most highly upregulated transcripts in grade IV GBMs compared to grade III gliomas.

Previous studies have yielded less than encouraging false positive and false negative rates resulting from the use of algorithms [ 17, 18] prompting an increasing focus on combining multiple expression profiling datasets with in silico target prediction [ 19].