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Several previous studies have considered the importance of similarity reduction in datasets of MHC-II peptides.
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Perfect conservation (i.e., 100% confidence in ancestral position identity), would result in a large reduction in dataset size due to the "false negative" exclusion of many positions due to the presence of these occasional derived states.
In some cases, the overall reduction in dataset size per locus was considerable (e.g., 21.9% [ 50], 13.6% [ 51], up to 8.5% [ 52]).
Despite the important reduction in dataset size, focusing on the most informative genes (2545 genes with at least four polymorphic sites) allowed us to compare the gene FI distribution in 105 different functional classes, each containing at least 40 different genes.
Aside from the influence-based assessment, similarity graph and collapsing simplicial complexes in network structures are used for topological network reduction in big datasets.
This resulted in a reduction in the dataset from 3308 to 662 farm records.
The occurrence of the first age-position in the datasets 'trimmed' from the left (youngest samples) was shifted around three years to the right, for each 10 years of reduction in the dataset age range.
The alert reduction was dramatic in datasets from small facilities, typically reducing the alert rate from 20 per year to below 5.
We were surprised to find that the relationship between blood pressure reduction and mortality reduction in our stroke dataset was too poor for an STE to be estimated.
Using the binary equation representation, it is easy to design a computer program to identify the possible reductions in a dataset by looking for patterns that differ from each other in just one position, i.e., the values are the same except for one variable.
In our case, we found that setting achieves the best reduction in across the three datasets explored here (Supplementary Figs S4 S6 and Supplementary Table S1).
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