False-positive/negative rates

For qualitative methods the false positives/negatives rate may be determined. 

Section II,B, this may be an overestimate of the false positive error rate because many of the apparent consecutive errors correspond to regions of disorder that are ordered in the crystal due to ligand binding or crystal contacts. Also, because disordered regions of length >40 residues are often missed due to false negative predictions of order, the data in... 

Sensitivity of immunological stain = 96% = 0.96 False-negative error rate of the test = 4% = 0.04 Specificity of the test = 94% = 0.94 False-positive error rate of the test = 6% = 0.06 Prevalence of effect in the tissues = 1% = 0.01... 

The importance of small troponin increases has been confirmed by their association with a poor prognosis. Based on several studies that compared CK-MB and cardiac troponin assays in patients with ACS, a substantial increase in rate of Mis ranging from 12% to 127% was detected. In one study of 1719 patients with ACS presenting to rule in/rule out MI, a subset (5%) of cTnl-negative but CK-MB-positive patients revealed the potentially underlying false-positive MI rate when using CK-MB as a standard for MI detection. This was likely due to release of CK-MB from skeletal muscle, in the absence of myocardial injury. Further, a subset (12%) of... 

Equation (3), which is an application of Bayes theorem, is referred to as the Positive Predictive Value. The parameter p is unknown but believed to be very small (<0.01) for large virtual libraries. 1 - p is the power (or 1 - type II error, where ft is the false negative error rate) and a is the type I error, also called the size of a test in the hypothesis testing context, or the false positive error rate. The last equation defines the probability that a molecule is determined to be a hit in a biochemical assay given that the virtual screen predicts the molecule to be a hit. This probability is of great interest because it is valuable to have an estimate of the hit rate one can expect for a subset of molecules that are selected by a virtual screen. 

It is extremely important to note that false negatives, for whatever reason, from a detector could prove to be fatal if the device cannot detect targeted toxic substances. Therefore, research emphasis should not only focus on reducing the false positive alarm rate. The potential for false negatives is more important and must be addressed. While false positives are nuisance factors, false negatives could lead to fatalities. 

According to this system, when 3-4 points are accumulated (depending on the application), then a confirmation is said to be made. However, human judgment should still play a role because the point assignments are arbitrary and actual rates of false positives and negatives are typically unknown. It makes sense that the more points that are gathered in an analysis should lead to a confirmation, but MS should not be considered as the only tool in the analysis, and all the pieces of information should make sense and point in the same direction for most accurate confirmations. 

PPR is the probability of obtaining positive responses, TPR true positive rate (Eq. 4.48), TNR true negative rate (Eq. 4.49), FPR false positive rate (Eq. 4.50), FNR false negative rate (Eq. 4.51), X/ and xu are the lower and upper limits of the unreliability region... 

These are well-known classification parameters true positive rate (p ), false positive rate (pnx), true negative rate (qnx), and false negative rate (qK). They can be easily obtained from the previous computations where we calculated the number of taxon and nontaxon members in each interval. For example, to calculate the true positive rate, we sum the number of taxon members in intervals above the hitmax, plus half of taxon members in the hitmax interval and divide this by the total number of taxon members in the sample. To calculate the false negative rate, we sum number of taxon members in intervals below the hitmax, plus half of the taxon members in the hitmax interval and divide this by the total number of taxon members in the sample. 

Minimizes both type I (false positive) and type II (false negative) error rates, thereby increasing power of the test statistic to be employed while decreasing inconsistent significant effects. 

Competition between components of a mixture becomes more likely as the hit rate and pool size increase. For a 10% hit rate, competing hits will be found in 26% of mixtures of ten compounds, but only 2.7% in mixtures of three [11]. Unless mixtures are deconvo-luted, competition can lead to false negatives when the competing compounds have different affinities. For screens using protein detection, competition can also produce false positives due to the additive effect of multiple weak binders. Competition can be controlled by limiting the size of mixtures and pooling dissimilar compounds to reduce the likelihood of two compounds binding at the same site. 

Table IV gives values of J when the false positive rate is 5 percent and the false negative rate is either 5 percent or 1 percent. J depends on V and, since K = 1, on the sura a + a . A...

Table IV. Sample Size (Value of J Given K = 1) Required CO Ensure a False Positive Rate of 5 Percent and a False Negative Rate of Either 5 Percent or 1 Percent When Comparing Two Means...

False positive rate (%) = false positives 100/total known negatives... 

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