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To address this concern, we examined the correlation between the number of measurements on specific genes and the gene maximum multiplicity measure on those genes.
The approach constructs a network of cancer types with a cancer maximum multiplicity measure establishing the similarity and dissimilarity between cancer types.
Although the COSMIC data set has limitations, the gene maximum multiplicity measure can be used to find candidate genes for investigation as systemic somatic mutations.
This suggests that prior knowledge selection bias misses genes systemically mutating across cancers that the gene maximum multiplicity measure can identify.
The measurement bias exhibited in COSMIC can be further examined when whole genome sequence data repositories for cancer [ 20] become sufficiently populated to apply the gene maximum multiplicity measure.
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Gene maximum multiplicity serves as a measure of a gene's systemic occurrence and hence a gene's potential to be a driver mutation rather than a passenger mutation.
The simpler one-dimensional measure, gene maximum multiplicity alone, provides none of this information.
A significant positive correlation was found between gene maximum multiplicity and the frequency of gene measurement (r p(487) = 0.74, Pearson's t = 23.9, p < 0.05).
This is only a 0.01 improvement over the one-dimensional gene maximum multiplicity clustering, so gene maximum multiplicity is the primary measure generating the clusters that differentiate germline mutations from non-germline mutations.
This relationship specifies a minimum and a maximum multiplicity.
(D) The maximum multiplicity is when there is no organization because any book can be in any position.
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