Sentence examples for mutation prediction algorithms from inspiring English sources

Algorithm performance and VUS predictions were compared to established phylogenetic based mutation prediction algorithms.

The workflow of our PSAAP algorithm is summarized in Figure 3. Next, both curated RET mutations (known disease association) and RET uncertain variants (VUS data) were analyzed and compared using four existing mutation prediction algorithms.

To gauge the performance of mutation prediction algorithms, we compared our empirical findings with results from commonly used algorithms.

As the pace of variant discovery currently far outstrips functional characterization, analyses will likely continue to focus on non-synonymous variants classified by computational mutation prediction algorithms.

The success of a consensus approach is also expected to vary with the sensitivity and accuracy of the somatic mutation prediction algorithms.

However, currently, most large-scale sequencing or genotyping studies rely upon mutation prediction algorithms to assign functionality to non-synonymous variants.

The introduction of PTEN to BRCA1 mutation-prediction algorithms is supported by single-cell analyses of temporal somatic events in BRCA1 breast tumor tissue, which revealed that loss of PTEN is an early event in the development of BRCA1 basal-like tumors, whereas TP53 mutations occur first in most luminal BRCA1 tumors [ 12].

To further benchmark a gold standard of truth for RET mutation prediction, all five algorithms were used to classify this set of not yet seen variants.

Using the determined HLA types, public databases of cell line mutations, and existing HLA binding prediction algorithms, we predicted antigenic mutations in each cell line.

All RNA structure prediction algorithms predict that certain mutations will significantly disrupt structure.

Interestingly, all RNA secondary structure prediction algorithms predict that a small subset of mutations will have a large effect on secondary structure.