The FWER is the probability that there is at least one false positive for a given threshold.

Therefore, a threshold of 0.01 means that there is a 1% chance that the list of time series with a Bonferroni adjusted p-value below 0.01 contains a false positive .

The likelihood of false positives is greatly reduced, but so is the likelihood of identifying true positives.

The receiver operating characteristic (ROC) curve plots the true positive rate (TPR) as a function of the false positive rate (FPR) as the threshold for calling a time series as a positive is varied.

The TPR and FPR are the fractions of the 10,000 simulated or Gaussian noise time series determined to be rhythmic at a threshold, respectively, and the threshold is varied over the entire range of false positive scores, such that the FPR ranges from 0 to 1.

In such cases, we find that ANOVA, F24, and ITK_CYCLE consistently better distinguish true and false positives .

However, the resulting false positives in Fig 2D show that the information within these shape metrics is insufficient to distinguish the grinder with high specificity.

In other words, we aim to minimize false negatives while tolerating a moderate number of false positives .

Therefore, we optimize the SVM parameters via the minimization of an adjusted error rate that penalizes false negatives more than false positives (Fig 3B).

along with the selection of the most likely candidate in images with multiple positive classification results is used to eliminate these false positives .

The precision plot shows, given a value of Ml°, the number of true positives with MI>M|0 identified with deep sequencing divided by the number of true and false positives with MI>M|0 versus the percentage of all pairs with MI>M|°.

As additional evidence that we observe meaningful correlations derived from the deep sequencing using our bounding procedure to constrain the bivariate probabilities, we note that many of the apparent false positive pairs of mutations in protease identified in our analysis may be biologically important because these pairs contain at least one variant associated with PI-eXposure.

Moreover, in the top 5% of pairs with highest MI from deep sequencing, 34 of the 58 pairs identified as putative false positives involve at least one known resistance mutation.

First, the precision defined as the ratio between the true positives (TP; true drug-protein interactions found by nAnno-Lyze) and the sum of TP and false positives (FP, a link between a drug and a protein not in the PDB).

It is important to note that both the precision and coverage of our method depend dramatically on the definition of false positives for our predictions.

Aspirin (DB00945), also a known inhibitor of the human COX-1 and COX-2, results in false positive predictions (Table 4 and Fig.

The same pathway is used to find other proteases like the Airway trypsin-like protease 4 (Q6ZWK6) or the Trypsin-3 (P35030) resulting in several false positive predictions.