Can survival prediction be improved by merging gene expression data sets?

With the elements of K determined for mESC proliferation and transition between patterns of allelic Nanog expression and the single-cell gene expression model in place, we proceeded

The relative expression value for each knocked out gene was then computed from data in the corresponding dataset as the average expression in wild-type replicates across all probe

Consistent with our probe-level analysis results, inspection of the top 20 ranked (NTk Rank) gene sets, which reflected those gene sets determined by the sigPathway software to have

To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the

The experimental analysis conducted in the previous section aimed at comparing the algorithms designed by HEAD-DT with three baseline algorithms: CART, C4.5, and REPTree. We

Using gene expression proile data from the Gene Expression Omnibus database, we identiied diferentially expressed genes that can contribute to the pathology of CAD.. Further

Linear models can be enhanced by the incorporation of interaction terms, whose magnitude and significance tell us if and how gene expression deviates from additivity of the main

As expected, with the original (PEER normalized) expression data, all 31 L-SSMD gene sets had a larger SSMD than sets of randomly selected genes, while all 14 S-SSMD gene sets had