We then illustrate the benefits of Broad-Enrich across the same set of 55 datasets, concentrating on H3K4me1,-2 H3K4me1,-2K9me3, H3K27me3 and H3K79me2 in the GM12878 cell line.
Indeed, there exist few accuracy measures for risk prediction, and, to the best of our knowledge, no articles studied their agreement on the same set of methods and datasets.
These patterns have clinical implications in terms of disease-free survival time and are always determined by the same set of genes in all datasets [ 3].
Based on the bias we observed, we excluded all proteins involved in ribosome biogenesis and assembly (GO term GO 0042254) and re-evaluated the same set of datasets.
These data revealed that the peaks were caused by the same set of mutation pairs in both datasets.
Alternatively, one could draw multiple imputations based on observed distributions of the covariates and outcomes in the derivation dataset, and apply the same set of imputation models to the validation dataset (approach V2 in Table 7).
If the same set of landmarks is used, heterogeneous microarray datasets, which could not have been directly combined, can be integrated, because the correlation signatures of the genes have compatible dimensions.
In these datasets, the same set of samples is profiled on several layers of genomic activity, e.g. CNV, DM, GE and ME.
In these datasets, the same set of samples is profiled on several layers of genomic activities, e.g. copy number variation (CNV), DNA methylation (DM), GE and microRNA expression (ME).
Next, the same set of genomes was compared to Santa Pola dataset.
After reapplying the same set of analyses to eight real-life multiclass microarray datasets, we demonstrate how the DDP works for datasets with different number of classes.
