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This is the background mutation rate in M. We have compared the time complexity of MCMC, GA, and SAGA on selecting the submatrix of maximum weight.
These strains have the lowest number of unique SNPs and hence provide the best estimate for the maximum value of the background mutation rate in the organism.
At this time, we interpret the majority of these changes to be nonfunctional and perhaps related to an increase in background mutation rate in cancer, specifically in breast cancer.
In the past years, in gene level, several studies have been devoted to predict driver mutation with significantly higher mutation rate than background mutation rate in a large cohort of cancer patients.
Since the background mutation rate in such populations has been estimated to be as high as 60% [ 21] the availability of independent alleles for each gene is essential to confirm functional assignment.
Firstly, significantly mutated genes were identified using the MuSiC algorithm [ 17], which determines the significance of the observed mutation rate of each gene based on the background mutation rate in the sample cohort.
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The information from known co-varying genes is important for estimating the background mutation rates in the genomic regions where few mutation events are observed.
We believe the lower background mutation rate observed in open regions of the genome in this study is likely to be a result of these regions being more accessible to repair mechanisms.
As mentioned above, a single fixed background mutation rate was used in the simplest version while a more complex approach took into account the DNA context of each mutation to adjust for the heterogeneity of mutation rates under different DNA contexts.
Significantly mutated genes (SMGs) were defined as the somatic mutations with higher mutated frequency than background mutation rate.
The new method accounts for the functional impact of mutations on proteins, variation in background mutation rate among tumors and the redundancy of the genetic code.
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