Index of papers in March 2015 that mention
  • true positives
Alan L. Hutchison, Mark Maienschein-Cline, Andrew H. Chiang, S. M. Ali Tabei, Herman Gudjonson, Neil Bahroos, Ravi Allada, Aaron R. Dinner
E 3 A A g Time s 'r r a E A AA Time Time
The likelihood of false positives is greatly reduced, but so is the likelihood of identifying true positives .
Simulated data benchmarks
We use it to further assess the importance of considering asymmetric waveforms, and we eXplore how multiple hypothesis correction impacts the results when the true positives represent a relatively small fraction of the simulated time series, as we eXpect to be the case in genome-wide studies.
Simulated data benchmarks
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.
Simulated data benchmarks
A score of 1 indicates that a method correctly identified all true positives and true negatives, while a score of —1 indicates that a method yielded all false positives and false negatives.
true positives is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Francisco Martínez-Jiménez, Marc A. Marti-Renom
Discussion
Within the correctly predicted interactions (i.e., true positives ), we included Flurbiprofen and Ibuprofen detailed information about the network routes.
nAnnoLyze benchmark
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).
nAnnoLyze benchmarking
1A) with an optimal threshold at —2.5 local Z-score resulting in a precision of 0.63 and coverage of 0.19 corresponding to 1,148 true positive predictions (Fig.
true positives is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: