Sentence examples for mutation datasets have from inspiring English sources

Also Radivojac et al. (2008) found that somatic cancer mutation datasets have a significant enrichment for mutations disrupting phosphorylation sites.

Large datasets have found mutations in additional genes (including IDH1/2, DNMT3A, RUNX1, TET2, NRAS, MLL, and others) to confer adverse prognosis, but it remains unclear how modifications to treatment regimens or the use of stem cell transplantation will impact outcomes in these specific subtypes of AML.

In total these datasets have been viewed 1.4 million times, an average of almost 100 views per dataset.

However, each of these datasets has its own biases; in particular, they are enriched for mutations in well-known genes that have been widely studied in cancer.

Analysis of TCGA datasets has revealed that several solid tumours are driven by copy number rather than by somatic mutations.

Cross-comparison of our transcriptome data with previously identified mutations revealed that most genes from our dataset have not been mutated.

The clear separation of drug classes in Figure 3 for the Erlangen model could be related to increased number of viruses with highly mutated PR in the Erlangen data compared to the Monogram data, e.g. using the mutation list from [31] 61% (25%) of the sequences in the Erlangen dataset had at least one fivee) protease mutations compared to 28% 3%%) in the Monogram dataset.

Several studies also have shown that the selection pressures vary by mutation type and sequence location in cancer mutation datasets.

In other words, we assume that each dataset has an unknown percentage of misclassified mutations.

Here, our algorithm outperforms SIFT, PolyPhen-2 and Mutation Assessor across all mutation datasets.

The number of sequences from ARV-naïve persons infected with subtype B and non-B HIV-1 viruses in our analysis dataset has approximately doubled since the 2007 publication, increasing the confidence with which nonpolymorphic mutations can be identified.