Index of papers in PLOS Comp. Biol. that mention
  • true positives
Susan Dina Ghiassian, Jörg Menche, Albert-László Barabási
Biological validation analysis
Genes with common annotations are considered as true positives .
Biological validation analysis
The performance is based on the number of candidate genes that are considered true positives .
Biological validation analysis
To quantify the statistical significance of a given number of true positives at a given iteration step we use a sliding window approach: At each iteration step i, we consider the same number of candidate genes as there are seed genes for the respective disease.
Comparison with existing methods
a higher ratio of true positives TP/(TP+FP).
Discussion
This can be used to estimate the expected true positive rate in the predictions and is particularly convenient for predicting new disease associations, where the total number of proteins involved in a disease is not known.
Estimating the recovery rate
As expected, the highest rate of true positives is achieved in early iterations, so the highest ranked proteins are most likely to be part of the original full module.
Estimating the recovery rate
Indeed, estimating the true positive rate is inherently difficult as the true set of proteins is by definition unknown.
Validating disease modules
Hence, the true positive rate can be estimated by removing varying fractions of seed proteins.
true positives is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
William F. Flynn, Max W. Chang, Zhiqiang Tan, Glenn Oliveira, Jinyun Yuan, Jason F. Okulicz, Bruce E. Torbett, Ronald M. Levy
Correlation analysis in using bound estimates protease captures known pair correlations
From these 1275 pairs, the 127 (top 10%) pairs with the highest MI when calculated using the MSA were selected as the putative true positives to which we compared our procedure.
Correlation analysis in using bound estimates protease captures known pair correlations
In total, 1275 pairs are plotted (127 putative true positives ) that are common to both our deep sequencing dataset and the Stanford HIVDB downloadable protease dataset (see Materials and Methods).
Correlation analysis in using bound estimates protease captures known pair correlations
True positives were determined through a mutual information calculation similar to the calculations in [3].
Supporting Information
As in Fig 5, shown are the top 5% of 1275 pairs With 127 putative true positives from Stanford HIVDB.
Supporting Information
As in Fig 5 and S3 Fig, shown are the top 5% of 1275 pairs With 127 putative true positives from Stanford HIVDB.
true positives is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
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: