1 tion of errors of prediction (SDEP) of 0.51 (
leave-one-out).
2 odel was built and validated by the standard
leave one out analysis.
3 Leave-one-out analysis demonstrated that the gray matter
4 A
leave-one-out analysis reveals that these models predict
5 on performance of TPCR was evaluated by both
leave-one-out and leave-half-out cross-validation using
6 ntiating the two case groups was assessed by
leave-one-out and Monte Carlo cross-validations.
7 diction algorithm based on these genes and a
leave-one-out approach, we assigned sample class to thes
8 two principal components was assessed by the
leave-one-out approach.
9 l operating condition (NOC) samples, using a
leave-one-out approach.
10 Using a '
leave-one-out'
approach we find average success rates be
11 ine-learning classification system that uses
leave-one-out bias optimization and discriminates among
12 been used as a proof of concept for a novel "
leave-one-out"
biosensor design in which a protein that
13 n) or large bias (such as resubstitution and
leave-one-out bootstrap).
14 The technique was cross (
leave-one-out),
both internally and externally, validate
15 ies from benign lesions was evaluated with a
leave-one-out-
by-case analysis.
16 e classifier, and then computes whether this
leave-one-out classifier correctly classifies the delete
17 A
leave-one-out classifier successfully distinguished auti
18 We achieve a low
leave one out cross validation error of <10% for the can
19 characterized by the values of the internal
leave one out cross-validated R2 (q2) for the training s
20 ME model of groundwater (222)Rn results in a
leave-one out cross-validation r(2) of 0.46 (Pearson cor
21 The
leave-one-out cross validation (LOOCV) method was implem
22 The model was supported by (i)
leave-one-out cross validation and (ii) division into th
23 Leave-one-out cross validation and case studies have sho
24 Through
leave-one-out cross validation and cross-classification
25 The model was validated by
leave-one-out cross validation and showed good recogniti
26 The model was validated with a
leave-one-out cross validation procedure.
27 A PNC LUR model (R(2) = 0.48,
leave-one-out cross validation R(2) = 0.32) including tr
28 We use the
leave-one-out cross validation to compare the performanc
29 tive Mendelian relationship in families, and
leave-one-out cross validation to verify our results.
30 A
leave-one-out cross validation was used to assess the ac
31 The resulting model was validated by
leave-one-out cross validation.
32 ity and specificity were calculated by using
leave-one-out cross validation.
33 d by using logistic regression analyses with
leave-one-out cross validation.
34 individual sequence pairs, and is tested by
leave-one-out cross validation.
35 tic curves and was further assessed by using
leave-one-out cross validation.
36 The
leave-one-out cross-validated coefficients q(2) for the
37 The best models were characterized by the
leave-one-out cross-validated correlation coefficient q(
38 Leave-one-out cross-validated partial least-squares give
39 model that was evaluated on the basis of its
leave-one-out cross-validated partial least-squares valu
40 characterized by high internal accuracy with
leave-one-out cross-validated R(2) (q(2)) values ranging
41 high internal accuracy were generated, with
leave-one-out cross-validated R(2) (q(2)) values ranging
42 The coefficient of determination (R(2)) and
leave-one-out cross-validation (LOOCV) demonstrate good
43 SEA) to evaluate the clustering results; (2)
Leave-one-out cross-validation (LOOCV) to ensure that th
44 In these small samples,
leave-one-out cross-validation (LOOCV), 10-fold cross-va
45 rker genes while offering the same or better
leave-one-out cross-validation accuracy compared with ap
46 highly disordered, and displayed comparable
leave-one-out cross-validation accuracy.
47 fier for this training set that achieves 87%
leave-one-out cross-validation accuracy.
48 We used split-half and
leave-one-out cross-validation analyses in large MRI dat
49 The
leave-one-out cross-validation analysis of the data from
50 A
leave-one-out cross-validation analysis was used identif
51 In
leave-one-out cross-validation analysis, ten of 11 sensi
52 y; r(2) = 0.93 and a q(2) = 0.91 utilizing a
leave-one-out cross-validation analysis.
53 of 88.0%, a finding that was confirmed using
leave-one-out cross-validation analysis.
54 Leave-one-out cross-validation and an independent data s
55 s estimated and subsequently validated using
leave-one-out cross-validation and data from the Multice
56 In the
leave-one-out cross-validation and de novo gene predicti
57 performance of sNebula on this dataset using
leave-one-out cross-validation and five-fold cross-valid
58 Leave-one-out cross-validation and gene pairing analysis
59 gorous benchmarking of CCN-BLPred using both
leave-one-out cross-validation and independent test sets
60 A
leave-one-out cross-validation approach is used to deter
61 est classification algorithm combined with a
leave-one-out cross-validation approach was implemented
62 ome for 85/102 (83%) NB patients through the
leave-one-out cross-validation approach.
63 Repeat analysis by using
leave-one-out cross-validation decreased the apparent di
64 Leave-one-out cross-validation experiments show that pre
65 hold-out testing methods: a nearly unbiased
leave-one-out cross-validation for the 60 training compo
66 Leave-one-out cross-validation indicated high predictive
67 accuracy of these models was assessed by the
leave-one-out cross-validation method.
68 (PLS) discriminant analyses, validated by a
leave-one-out cross-validation method.
69 f the classifications was performed with the
leave-one-out cross-validation method.
70 Various statistical methods, including
leave-one-out cross-validation methods, were applied to
71 We evaluated the rcNet algorithms with
leave-one-out cross-validation on Online Mendelian Inher
72 r neurological disease, with 87% accuracy by
leave-one-out cross-validation on training data (N = 23)
73 lites, we tested 148 metabolites following a
leave-one-out cross-validation procedure or by using MS/
74 A
leave-one-out cross-validation procedure using the top 2
75 Using a
leave-one-out cross-validation procedure, we were able t
76 een produced with excellent accuracy using a
leave-one-out cross-validation process.
77 -redundant polytopic proteins using a strict
leave-one-out cross-validation protocol, MemBrain achiev
78 nalysis model built using these markers with
leave-one-out cross-validation provided a sensitivity of
79 LUR-BME results in a
leave-one-out cross-validation r2 of 0.74 and 0.33 for m
80 e corresponding carcinomatous lesions, and a
leave-one-out cross-validation showed a 98% correct pred
81 By applying a
leave-one-out cross-validation strategy, we could show t
82 ill achieved comparable success rates to the
leave-one-out cross-validation suggesting that sufficien
83 With use of a
leave-one-out cross-validation technique, this method wa
84 We used logistic regression with
leave-one-out cross-validation to predict outcomes, and
85 dimensionality reduction algorithm, and used
leave-one-out cross-validation to predict underlying pat
86 e "test" set of tumors, we used a supervised
leave-one-out cross-validation to test how well we could
87 This was used in a
leave-one-out cross-validation to train weights that opt
88 In
leave-one-out cross-validation using support vector mach
89 Leave-one-out cross-validation was applied to each tumor
90 Leave-one-out cross-validation was performed.
91 Leave-one-out cross-validation was used for validation.
92 gaussian process classifiers using a nested
leave-one-out cross-validation were used to predict the
93 tly classified 18 of the 21 classic cases in
leave-one-out cross-validation when compared with pathol
94 functions in the classifier can be chosen by
leave-one-out cross-validation with the aim of minimizin
95 redictors and initial severity combined with
leave-one-out cross-validation yielded a categorical pre
96 Machine-learning analyses (with
leave-one-out cross-validation) assessed whether speech
97 they either have large variability (such as
leave-one-out cross-validation) or large bias (such as r
98 NA binding and 6761 non-RNA binding domains (
leave-one-out cross-validation).
99 lysis, discriminant function analysis (DFA),
leave-one-out cross-validation, and Kendall coefficient
100 Logistic regression,
leave-one-out cross-validation, and receiver operating c
101 cells was determined by logistic regression,
leave-one-out cross-validation, and receiver operating c
102 Logistic regression analysis,
leave-one-out cross-validation, and receiver operating c
103 Logistic regression analysis,
leave-one-out cross-validation, and receiver operating c
104 In logistic regression,
leave-one-out cross-validation, and receiver-operating c
105 ted by combining data set bootstrapping with
leave-one-out cross-validation, with random sampling of
106 ing their respective benchmark datasets, and
leave-one-out cross-validation.
107 3 with approximately 91% accuracy, based on
leave-one-out cross-validation.
108 sion profiles was rigorously evaluated using
leave-one-out cross-validation.
109 estimated 100% predictive accuracy based on
leave-one-out cross-validation.
110 tors of overall survival were developed from
leave-one-out cross-validation.
111 anges in connectivity after TMS, followed by
leave-one-out cross-validation.
112 eralization of the findings was supported by
leave-one-out cross-validation.
113 mis and Bacillus subtilis, was confirmed via
leave-one-out cross-validation.
114 noma , and results were validated by using a
leave-one-out cross-validation.
115 eatures alone), with 84% accuracy in 5-fold,
leave-one-out cross-validation.
116 ng 89 bacterial species in our library using
leave-one-out cross-validation.
117 of 60 samples not used for discovery, using
leave-one-out cross-validation.
118 elated patterns with logistic regression and
leave-one-out cross-validation.
119 tures but have similar levels of accuracy in
leave-one-out cross-validations (LOOCV).
120 pifarnib with the greatest accuracy using a "
leave one out"
cross validation (LOOCV; 96%).
121 e data set out", similar to the traditional "
leave one out"
cross-validation procedure employed in pa
122 tion and 80% in prediction ability by using "
leave-one-out"
cross-validation procedure.
123 t independent training and testing sets, or '
leave-one-out'
cross-validation analysis with all tumors
124 d use regression models (LUR) frequently use
leave-one-out-
cross-validation (LOOCV) to assess model f
125 UR models were evaluated using (1) internal "
leave-one-out-
cross-validation (LOOCV)" within the train
126 cation of 37 clinically relevant bacteria in
Leave-One-Out-
Cross-Validation.
127 of the ProtPair for IPS study as measured by
leave-one-out CV is 69.1%, which can be very beneficial
128 The
leave-one-out estimates of the probability of test error
129 First, a
leave-one-out experiment is used to optimize our method
130 Cross-validation (in
leave-one-out form) removes each observation in turn, co
131 n models was evaluated comparing the classic
leave-one-out internal validation with a more challengin
132 their predictive power was assessed using a "
leave one out"
jackknife cross-validation strategy.
133 In this study
Leave One Out (
LOO) cross validation is used for validat
134 ee receptors involving 202 complexes, with a
leave-one out (
LOO) cross-validated Q(2) of 0.689, was o
135 cies in 10-fold cross validation (10xCV) and
leave-one-out (
LOO) approaches, respectively.
136 -specific transcripts for EDMD, we applied a
leave-one-out (
LOO) cross-validation approach using LMNA
137 s of these two groups by the cross-validated
leave-one-out machine-learning algorithms revealed a mol
138 twork (BP-ANN) was trained in a round-robin (
leave-one-out)
manner to predict biopsy outcome from mam
139 g phase, which was cross-validated using the
leave-one out method.
140 The new models were validated by the
leave-one-out method and were cross-validated in a separ
141 s for predicting checkpoint function using a
leave-one-out method.
142 discriminant analysis classifier by using a
leave-one-out method.
143 e performance of these classifiers using the
leave-one-out method.
144 Models were evaluated using
leave-one-out (
n - 1) (LOOCV) and grouped (n - 25%) cros
145 d partial least squares training with either
leave-one-out or batch-to-batch testing.
146 The experiments presented herein utilize
leave-one-out partial least-squares (LOO-PLS) analysis t
147 (ANN) analysis of the combined data set in a
leave-one-out prediction strategy correctly predicted th
148 ~120,000 subjects) and MDD (using a 10-fold
leave-one-out procedure in the current sample), (ii) biv
149 in terms of the error rate obtained from the
leave-one-out procedure, and all of the forests are far
150 DeltaHB(total), and DeltaSASA, the r(2) and
leave-one-out q(2) are 0.69 and 0.67.
151 (2) of 0.72, an adjusted R(2) of 0.65, and a
leave-one-out Q(2) of 0.56.
152 ocked alignment) showed the best statistics:
leave-one-out q(2) of 0.616, r(2) of 0.949, and r(2)pred
153 Cross validation (
leave-one-out technique) was applied to the data.
154 ation accuracy in a cross validation using a
leave-one-out technique.
155 The concordance of the
leave-one-out test is over 99.5% and is 99.9% higher for
156 In
leave-one-out tests, an average of 67% of drugs were cor
157 In a
leave-one-out three-way classification analysis, the mod
158 and specificity values of 100% employing the
leave-one-out validation method.
159 For biological process, the
leave-one-out validation procedure shows 52% precision a
160 f our prediction is measured by applying the
leave-one-out validation procedure to a functional path
161 Leave-one-out validation showed classification accuracy
162 ine achieved high classification scores in a
leave-one-out validation test reaching >90% in some case
163 Subsequent evaluation of the model using
leave-one-out validation yielded a classification accura
164 dologies (2-fold, repeat random subsampling,
leave one out)
were utilized to determine the performanc