False negative rate

False negative rate probability that the test result is negative when the analyte is present... 

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. 

The reality of HTS is that the true statistical hit rate is often greater than the operational hit rate that can be accommodated by confirmation assays. In such a case, the use of a Top X method carries a number of significant drawbacks, the most significant of which is the creation of artificially high false negative rates through the neglect of actives less potent than the cutoff used. This is often overlooked but is of critical importance. 

Mass spectrometry enables the type of direct analyses described, but it does have its limitations. Online operation forces detection at infusion concentrations, in salty buffer and under complex mixture conditions. General ion suppression results from the buffer and mixture components, and mixture complexity can tax the resolution of even the best mass spectrometers. Increasing compound concentration is not the answer, as this leads to problems of solubility and increased compound consumption. We have found that the online method can work successfully for up to 100 compounds per analysis, but the false negative rate becomes appreciable [21]. As an alternative for ligand discovery purposes, we have developed a FAC-LC/MS system in which FAC effluent is sampled and analyzed by LC/MS [19]. This system offers the ability to concentrate mixture components and introduces another dimension to the data in order to tolerate more complex mixtures (Fig. 6.9). Using this system, we have screened approximately 1000 modified trisaccharide acceptor analogs targeting immobilized N-... 

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...

For example, if the coefficient of variation is 2 and the level is twice the other then seventy-three samples are required to achieve a false negative rate of 5 percent. To achieve a false negative rate of 1 percent, one hundred and twelve samples would be required. As the acceptable difference between levels increase, the required number of sample required decreases. When one level is one hundred times the other and the coefficient of variation is 1, only three samples are required to achieve a false negative rate of 1 percent. 

So far we have assumed that both air levels would have to be determined and therefore that two sets of J samples would have to be collected. In some cases one level may already be available from other records and can be used as a standard of comparison. Table VI shows for this special case how the probability of a false negative depends on the number of samples collected. For small differences between the measured level and the standard a small sample size has an unacceptable high false negative rate. For example, if the mean is five times the standard level and the coefficient of variation is... 

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

Once the assay is validated by these two sets of compounds, it can be used to test a larger group of marketed compounds, which will reveal the performance of the assay with an unbiased set of diverse, already well characterized compounds. False positive and false negative rates can be often defined with this validation. 

True-positive and false-negative rates of the LEAP1 method as a function of search threshold for molecular similarity... 

Table 13.3 shows the performance comparison of LEAP1 method vs. the exhaustive search. The speedup factor is the ratio between search times required by the exhaustive search and LEAP1. It is seen that in exchange for a 6% false-negative rate we can get more than a 27,000-fold speedup. If we assume that... 

In a second row, when the AUCs of the two retained pharmacophore models are compared, hypothesis 1 appears to perform better than hypothesis 2. Not only is its AUC larger when compared with hypothesis 2, but also its ROC curve shape is more interesting. Hence, if sensitivity is to be maximized to reduce the false negative rate and increase the chances of finding novel leads (Se=l), it is possible to increase the specificity to 0.73 with hypothesis 1 (point Fig. 15.11), whereas the highest possible specificity value for Se=l is only 0.36 with hypothesis 2 (point S2, Fig. 15.11). In other words, if we analyze the... 

Neural network methods for predicting whether screened molecules are CYP450 2D6 substrates have been developed by GlaxoSmithKline researchers. A 20% false positive and a 10% false negative rate were stated (Ekins and Rose, 2002). 

We compared the assay results for 80 common chemicals from both the Nishihara et al. and NCTR data sets inconsistent assay results were observed for 12 chemicals. Specifically, of 30 active chemicals in the Nishihara et al. data set, one chemical was found inactive in the NCTR data set of 50 inactive chemicals in the Nishihara et al. data set, 11 chemicals were found active in the NCTR data set. These observations show that even using the experimental data from the ER binding assay (the NCTR data set) to predict the experimental results from the yeast two-hybrid assay (the Nishihara et al. data set), there may be about a 15% (12/80) discrepancy, or 3.3% (1/30) false negative rate and 22% (11/50) false positive rate. Care should be taken in interpreting the QSAR validation results using this data set (Hong et al., 2002). 

This phase classifies chemicals passing from the previous phase into active and inactive categories. Three structural alerts (Section IV.B), seven pharmacophore queries (Section IV.C), and the Decision Tree classification model (Section IV.D) were used in parallel to discriminate active from inactive chemicals. To ensure the lowest false negative rate in this phase, a chemical predicted to be active by any of these 11 models is subsequently evaluated in Phase III, whereas only those predicted to inactive by all these models are eliminated for further evaluation. Since structural alert, pharamacophore and Decision Tree methods incorporate and weight differently the various structural features that endow a chemical with the ability to bind the ER the combined outputs derived... 

Model Sensitivity Specificity Concordance False Positive Rate False Negative Rate... 

Balancing these three rates equally yields an optimal threshold of 0.846. Both thresholds are indicated by vertical lines in Figure 8. Figure 9 shows the actual predicted scores for the molecules that do cross the blood-brain barrier (BBB+) as well as those that do not (BBB-). The false positive and false negative rates are, of course, direct consequences of which threshold is chosen. The appropriate threshold depends on the goal. For example, if the project is a neuroscience project where BBB+ is the goal, it may be that the team wants to find and reject... 

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