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For further validation of linkages among markers, the genotyping data of markers grouped by MAPMAKER were analyzed through JoinMap 4.1 (http://www.kyazma.nl/index.php/mc.JoinMap) at higher logarithm of odds (LOD) threshold (4.0 8.0) with Kosambi mapping function.
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They also served as a validation of linkage group assignment.
Using other data sources, BRIDGE achieves AUC scores between 76.02% and 79.08% in the validation of linkage intervals.
For example, using the PPI data alone, BRIDGE achieves an AUC of 86.08% in the validation of linkage intervals and 82.23% in the validation of random controls.
Using gene expression data alone, BRIDGE achieves an AUC of 74.30% in the validation of linkage intervals and 69.04% in the validation of random controls.
For example, in the validation of linkage intervals, 47.41% test genes are ranked first by using PPI data alone, while only 23.25% are ranked first by using gene expression data (see Table 1 for more details).
Also, validation of the linkage of various episodes of care must be ascertained.
In the validation of a linkage interval, we obtain an MRR of 14.21% and an AUC of 86.65%.
Therefore, when developing a method for linking two or more datasets with limited identifying information, validation of the linkage is vital.
Second, in the validation of simulated linkage intervals, we prioritize domains that are known to be associated with disease phenotypes (i.e., seed domains) against domains that are located around the seed domains.
To describe the technical approach and subsequent validation of the probabilistic linkage of the three anonymous, population-based Dutch Perinatal Registries (LVR1 of midwives, LVR2 of obstetricians, and LNR of pediatricians/neonatologists).
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