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The expected number of data sets transmitted was: 15 daily ED data set ×92 days in the surveillance period = 1,380 expected data sets.
During ONAP, the stability was 100% (195 expected data sets and 195 received), and the stability for the OFAP was 99.40% (1,185 data sets expected, 1,178 received).
The chi-square test is a statistical tool to evaluate the difference between the observed and expected data sets under specific hypothetical conditions.
The results indicate that DBM-PSO can successfully use the chi-square test to identify good models by evaluating the difference between the observed and expected data sets under specific hypothetical conditions.
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The expected data set transmitted was based on the number of participating EDs, calculated on the basis of the length of the surveillance period in days (number of daily ED data sets ×92 days of surveillance).
As expected, data set C1 (same genes) sequence pairs had the lowest and most tightly clustered difference in coding potential (median = 0.08, IQR = 0.22), with only 11% had a difference of greater than 0.5.
This benefit can be expected for data sets acquired using SNP arrays as well as data from sequencing.
Due to the great variability in disease-related impairment scales and disability and HRQL instruments used in patient groups with a wide range of disorders, we expected heterogeneous data sets.
This might somewhat mimic part of the ascertainment bias expected in real data sets, since monomorphic SNPs are not expected to be genotyped or analyzed [12].
We expect our data set to be biased toward polymorphisms with minor fitness effects because the sequenced DPGP genomes were prepared as inbred strains (RAL populations) or strains with targeted pairs of homozygous chromosomes (MW populations).
However, if LSn increases as genes are concatenated this suggests there is emergent support for that node because support is increasing beyond what we would be expect as data set size increases.
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