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1  k-fold cross-validation, bootstrapping, and jackknifing.
2 a prediction rule that was evaluated through jackknifing.
3 espective confidence limits are estimated by jackknifing.
4  built from the full alignment and from each jackknifed alignment, and then the likelihood for each t
5                LumberJack creates non-random jackknifed alignments by progressively sliding a window
6 was performed on a per-nodule basis by using jackknife alternative free-response receiver operating c
7 aracteristic analysis was performed by using jackknife alternative free-response receiver operating c
8 e was quantified by using the area under the jackknife alternative free-response receiver operator ch
9  correctly classified 72% of participants in jackknife analyses.
10                                              Jackknife analysis of CAC results in the training data s
11                                              Jackknife analysis provided estimates of statistical sig
12                             We implemented a jackknife analysis to identify those species most influe
13                                              Jackknife analysis was used for prediction of probabilit
14                                          LDA jackknife analysis, a statistical resampling technique,
15                                              Jackknife and cross-study validation confirmed the robus
16 d to standard resampling methodology such as jackknifing and noise perturbation.
17    Results obtained through re-substitution, jackknife, and independent data set tests, respectively,
18                    Instead, a leave-data-out jackknife approach better estimates the influence of a g
19 en when 35% of the data were left out in the jackknife approach, the confidence levels of SHAPE-direc
20 pean, and Asian descent and to predict, by a jackknife-based approach, the amount of genetic diversit
21  Under The InfluencE (CUTIE), an open-source jackknifing-based method to detect such cases with both
22 to the quality of input according to a novel jackknife confidence scoring.
23  (1) intragroup pattern uniformity by use of jackknife correlation coefficient analysis of the integr
24 s evident between the two automated systems (jackknife correlation r = PL 0.77 [95% confidence interv
25 ntal observed and predicted folding rates in jackknife cross validation.
26 e power was assessed using a "leave one out" jackknife cross-validation strategy.
27 nD-PseAA predictor on such a data set by the jackknife cross-validation test was 85% for the case in
28 f iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmar
29 class of candidate models, then we apply the jackknife cross-validation to optimize model weights for
30         With a combination of 6 variables, a jackknifed cross-validation test found the probability o
31 normally distributed and admits a consistent jackknife estimator of its variance.
32  using the Flury hierarchical method and the jackknife followed by MANOVA method.
33                                          The jackknife free-response receiver operating characteristi
34                                            A jackknifing free response operating characteristic (JAFR
35 r Min proteins induced frequent and dramatic jackknife-like bending of cells at division septa, with
36                          We hypothesise that jackknife-like conformational changes in SlaA play impor
37 analysis can be done using the data from the jackknife method but the estimated power is typically a
38   SPLUS coding for the implementation of the jackknife method is provided.
39                                            A jackknife method was used because any patient may have m
40                                          The jackknife method was used for bias correction.
41           The model was cross-validated by a jackknife method, and performance was evaluated with the
42 g characteristic (ROC) analysis by using the jackknife method.
43 method was validated using the leave-one-out Jackknife method.
44 ors such as Capture recapture, Chao, ACE and Jackknife methods.
45                                 We present a jackknife model for ligase III that posits conformationa
46            The collective results support a "jackknife model" in which the ZnF loads ligase III onto
47 ion through the variance estimator using the jackknife, one of resampling techniques.
48 e whole course of the operation in the prone jackknife position without anesthetic-associated complic
49 trocar technique with the patient in a prone jackknife position.
50 he paired sire and dam pseudovalues from the jackknife procedure and the likelihood ratio test from t
51 alyze the effect of the sampling size of the Jackknife procedure on the GCL statistics.
52                  Logistic regression and the jackknife procedure were used to select correlates of on
53 in the exposure explained by all SNPs, block-jackknifing PRS did not suffer from overfitting bias (me
54 urvival time is transformed into a series of jackknife pseudo observations and then used as quantitat
55 OHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily
56 rametric resampling method, an index (called jackknifed reliability index, JRI) was proposed, and emp
57       SEs and 95% CIs were assessed by using jackknife replicate weights.
58 of serum analyte by using sample weights and jackknife replication methods to adjust for the complex
59                   Here, we evaluated a block jackknife resampling framework for genome-wide associati
60 verlapping GWAS sample and (3) using a block jackknife resampling framework.
61                                  Using block jackknife resampling MR in an applied analysis, we exami
62 ighted multiple linear regression models and jackknife resampling, with a 3-segment time component, w
63 RS mean R2 = 0.086), whereas estimates using jackknife score remained robust to overfitting (mean R2
64 s remained largely unchanged in quartile and jackknife sensitivity analyses.
65 iring adjustment as in Efron (2004) or using jackknife standard errors.
66 ernal validity of the predictive gene set, a jackknife step is used.
67                                          The Jackknife tenfold cross-validation was used for training
68    It is shown by the self-consistency test, jackknife test and independent dataset tests that the su
69                          The success rate by jackknife test for the 139 serine hydrolases was 85%, im
70              The overall success rate by the jackknife test for the identification between enzyme and
71              The overall success rate of the jackknife test for the plant protein dataset was 86%, an
72                        It is observed in the jackknife test that the accuracy achieved by the propose
73 L (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminate
74                                  Accuracy of jackknife test, 10-fold cross-validation test and indepe
75 s are obtained by the self-consistency test, jackknife test, and independent dataset test, respective
76                    As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.9
77                                         In a jackknife test, EFICAz shows high accuracy (92%) and sen
78 cleotide composition were 80-90% accurate in jackknife testing experiments for bacteria and 90-99% fo
79 -based potentials is evaluated by using four jackknife tests and by assessing the potentials' ability
80 as very good (AUC = 0.885, TSS = 0.695), and jackknife tests of variable importance showed that the c
81 t performance (AUC = 0.936, TSS = 0.823) and jackknife tests showed that precipitation seasonality (B
82 n performance on 5-fold cross-validation and jackknife tests.
83 re obtained by both the self-consistency and jackknife tests.
84 sensitive to variation in network scale, and jackknifing the UK MSM dataset to the size of the Swiss
85  and 545 non DNA-binding) proteins and using jackknife validation, StackDPPred achieved an ACC of 89.
86 chi(2) tests adjusted for study weights with jackknife variance.
87 A prediction model based on data resampling (Jackknife) was applied, and prediction values for select