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In this paper we offer alternatives to the average patient standard that reduce the risk of false negative without increasing false positives from drug company opportunism.
Given the danger of misidentifying a cancer patient (which could lead to death), tests are generally designed to ensure a high sensitivity by decreasing specificity (i.e. increasing false positives to ensure that as many positives are identified).
HF1 was too stringent, and HF3/HF4 tended to either suffer from increasing false positives or increasing false negatives as the number of gene trees increased.
Furthermore, mammalian cis-regulatory elements can be tens of kilobases from transcription start sites, necessitating searching large sequence regions and further increasing false positives.
Comparing the allele frequency in a single segregant bulk after phenotypic selection to a null frequency of 0.5 to detect causative mutations should be avoided, however, because any effects of a genetic variant on fitness can cause allele frequencies to deviate from this null model, increasing false positives.
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When identifying at-risk students, it is important to minimize false negative (i.e., type II) error while not increasing false positive (i.e., type I) error significantly.
Additionally, the average rank decreases with an increasing false positive fraction.
The influence of this bias is reduced with increasing false positive fraction and the more robust methods became apparent.
With increasing false positive rates this bias is reduced and the gap between the optimal assignment methods and the literature results is successively closed.
Increasing the sensitivity of detection usually carries a cost of increasing false positive identifications.
The ROC curve shows the extent to which the method is able to obtain as many positive results (increase of sensitivity) as possible at the expense of increasing false positive predictions (decrease of specificity).
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