ライフサイエンスコーパス: cross-validation

1 atures ranged from 83% to 91% (after 10-fold cross validation).

2 nts far from monitoring locations (clustered cross-validation).

3 tus from genetic data (prediction R2=0.12 in cross-validations).

4 sulting model was validated by leave-one-out cross validation.

5 icity were calculated by using leave-one-out cross validation.

6 luations were carried out using leave-oneout cross validation.

7 core (macro weighted) 0.760 with Monte Carlo cross validation.

8 4.30% and 0.86, respectively, using 200 fold cross validation.

9 ival analysis, the area under the curve, and cross validation.

10 which were all statistically validated using cross validation.

11 the effectors of G proteins by using 10-fold cross-validation.

12 r conclusive predictions with further K-fold cross-validation.

13 ix were found to be selected in all folds of cross-validation.

14 l survival were developed from leave-one-out cross-validation.

15 arides presented high R(2) and low errors of cross-validation.

16 accurate than models tested by within-study cross-validation.

17 standard techniques, including AIC, BIC and cross-validation.

18 linically relevant bacteria in Leave-One-Out-Cross-Validation.

19 iobjective optimization, in combination with cross-validation.

20 ial-only model was computed by using 10-fold cross-validation.

21 the findings was supported by leave-one-out cross-validation.

22 Results were validated by means of 10-fold cross-validation.

23 essed a classifier trained on RNAseq data by cross-validation.

24 r operating characteristic curve (AUROC) and cross-validation.

25 the Vineland score with an R2 of 0.45 after cross-validation.

26 tes detection using leave-one-individual-out cross-validation.

27 tic curve and was validated using three-fold cross-validation.

28 matrices on the Baker's yeast PPI network in cross-validation.

29 than 80% of correct predictions in leave-one cross-validation.

30 r relationships from connectivity data using cross-validation.

31 ctivity after TMS, followed by leave-one-out cross-validation.

32 eiver operating characteristic curve, and by cross-validation.

33 acteristic) based on the 5 trials of 10-fold cross-validation.

34 us subtilis, was confirmed via leave-one-out cross-validation.

35 , with 84% accuracy in 5-fold, leave-one-out cross-validation.

36 mance was internally validated using 10-fold cross-validation.

37 l species in our library using leave-one-out cross-validation.

38 existing PSSM encoding methods by five-fold cross-validation.

39 and test data after model fitting and after cross-validation.

40 internally validated by use of bootstrap and cross-validation.

41 malignant lesions with leave-one-patient-out cross-validation.

42 imately 91% accuracy, based on leave-one-out cross-validation.

43 d verified their performance through 10-fold cross validations.

44 n coefficient of 0.9 in leave-one-tissue-out cross-validations.

45 are error 1.2 kcal/mol in the mutation-based cross-validations.

46 leave-one-out cross-validation and five-fold cross-validations.

47 mates the resulting classification error by (cross-) validation.

48 evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 10

49 .88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approac

50 rom those with NC function (with an averaged cross-validation accuracy of 76.3%, sensitivity of 69.4%

51 ered, and displayed comparable leave-one-out cross-validation accuracy.

52 ew computationally efficient and data-driven cross-validation algorithm.

53 We performed a 70/30 cross validation analysis to assess the accuracy of the

54 ediction root-mean-square error in a 10-fold cross validation analysis.

55 Cross-validation analysis revealed that microbiota expla

56 rmance of this method was measured through a cross-validation analysis using the Gene Ontology (GO) a

57 A leave-one-out cross-validation analysis was used identify a gene expre

58 eving a correlation of up to 0.86 on 10-fold cross validation and 0.80 in blind tests, performing as

59 Leave-one-out cross validation and case studies have shown that the pr

60 ILRMR performs better than other methods in cross validation and case studies.

61 Through leave-one-out cross validation and cross-classification on independent

62 roposed method achieves sound performance of cross validation and independent test.

63 chieved a consistent performance for 10-fold cross validation and two independent tests.

64 All of these results emerge from nested (cross-)validation and are supposed to reflect the model'

65 g a correlation coefficient of up to 0.70 on cross-validation and 0.68 on blind-tests, outperforming

66 diction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte C

67 tically validated with leave-one-patient-out cross-validation and absolutely quantified by selected r

68 These models were validated using both full cross-validation and an independent sample set giving st

69 racy and risk calibration of our model using cross-validation and compared its performance with model

70 In the leave-one-out cross-validation and de novo gene prediction analysis, o

71 sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations.

72 allow for adaptive specification search via cross-validation and flexible nonparametric regression a

73 rking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred p

74 hmarking experiments based on both five-fold cross-validation and independent tests indicated that th

75 s and yields a robust performance by 10-fold cross-validation and independent tests on both FS indels

76 performance of our method using both 5-fold cross-validation and independent tests.

77 In cross-validation and independent validation, the server

78 Through computational cross-validation and literature validation, we show that

79 Cross-validation and meaningful enrichment of gene ontol

80 tic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set.

81 achieved 91% accuracy on leave-one-study-out cross-validation and on three independent data sets.

82 alidated using a number of compounds in both cross-validation and test setting.

83 Five-fold cross-validation and the Area Under Receiver Operating C

84 associations with phenotype paths in HPO in cross-validation and the prediction of the most recent a

85 y >99% of the spectra during calibration and cross-validation and to correctly predict 100% of oxyphi

86 ated our Bayesian inference approach through cross-validation and verified the computed chromatin con

87 eved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent

88 prediction model was evaluated with 10-fold cross-validation, and a test group of patients was studi

89 erent data processing methods, leave-10%-out cross-validation, and real-time classification of new da

90 tion algorithm combined with a leave-one-out cross-validation approach was implemented to assess the

91 ets with the relative weights derived from a cross-validation approach.

92 We estimated model performance with two cross-validation approaches: using randomly selected gro

93 10 times of 10-fold cross-validations are conducted to illustrate the effica

94 We achieve a cross-validation Area Under Curve of 0.85 for the Receiv

95 parisons, the support vector machine 10-fold cross-validation area under the curve was between 0.93-1

96 Previous works relied on cross validation as an estimate of classifier accuracy.

97 As opposed to proper cross-validation as realized with [Formula: see text], [

98 and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.1

99 SEEK uses a query-level cross-validation-based algorithm to automatically priori

100 Such orthogonal transduction provides cross-validation, better sensor sensitivity, and a large

101 odel exploration and verification, including cross validation, bootstrapping, and AUC manipulation.

102 ula: see text] and we compare it with k-fold cross-validation, bootstrapping, and jackknifing.

103 dictions in well-sampled areas (conventional cross-validation) but substantially improves predictions

104 Confocal microscopy provided cross validation by immunofluorescent staining of the co

105 t squares-discriminant analysis (PLS-DA) and cross-validation by bootstrapping, discriminated to vari

106 ment of this staining method and its initial cross-validation by comparison with infrared (IR) micros

107 breast tumors from black vs white patients (cross-validation C index, 0.878).

108 ytical and numerical methods, that classical cross-validation can have strong bias under separate sam

109 sing of specimens, established tools such as cross-validation can lead to a spurious estimate of the

110 Employing cross validation, classifier prediction and measured cli

111 f 69.4% and specificity of 81.8% with nested cross-validation considering the model selection bias).

112 In addition, the cross-validation correlation was calculated at 0.96 from

113 A cross validation (CV) mixed effects model revealed reaso

114 An outer loop subject-wise cross-validation (CV) method evaluated the performance o

115 Our results achieve an overall cross-validation (CV) R(2) value of 0.80.

116 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction mo

117 model fitting and out-of-sample validation (cross validation, CV).

118 Rigorous cross-validations demonstrate the superior prediction qu

119 Cross-validation demonstrates a wide range in optimized

120 multivariate logistic regression followed by cross-validation, enhanced the sensitivity and specifici

121 rate are typically evaluated on the basis of cross-validation error estimates in a few exemplary data

122 g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated

123 a subset of United States stocks resulted in cross-validation errors ranging from 0 to 5.3%.

124 el, which performed well in repeated 10-fold cross-validation, estimated total clearance, intercompar

125 In a cross-validation exercise, subcellular LA-ICPMS imaging

126 overfitting can be effectively controlled by cross validation experiment.

127 d an estimate for future data of 0.91 in the cross-validation experiment and correctly classified 9 o

128 In a cross-validation experiment in yeast, mouse and human, o

129 mediates conditional overexpression of BCL2 Cross-validation experiments in human DLBCL samples reve

130 cy of 96.4% was achieved for coffee types in cross-validation experiments.

131 ith features present in more than 50% of the cross-validation folds.

132 nt-family QTL analysis methods with fivefold cross-validation for 6 diverse traits using the maize ne

133 emonstrated >99.5% sex inference accuracy in cross-validation for 889 males and 5,361 females enrolle

134 fication errors of 2.4, 2.8, 2.8, and 11% by cross-validation for chloroform (7 stocks), thionyl chlo

135 lated overoptimistic findings and the use of cross-validation for error estimation in molecular class

136 ing methods: a nearly unbiased leave-one-out cross-validation for the 60 training compounds and an un

137 In terms of discrimination, from cross-validations for men, the PRIMROSE lipid model D st

138 and language scores from lesion maps, using cross-validation framework and a large (n = 90) database

139 ics and NRIs were calculated within a 5-fold cross-validation framework.

140 ns to unmonitored areas, spatially clustered cross-validation groups.

141 Machine learning with leave-one-subject-out cross-validation identified distributed neural activatio

142 Stratified five-fold cross-validation identified FA in the SN(FA-SNAv), CBF i

143 uantitative imaging features selected during cross-validation improved the model using conventional p

144 on the routine use of data normalization and cross-validation in such analysis.

145 ches to determine prediction performance are cross-validation, in which all available data are iterat

146 This cross-validation increases confidence in both methods fo

147 Rigorous cross-validations indicated that the new predictor remar

148 Our cross-validation indicates that the logistic regression

149 Using cross-validation, individual predictions were compared t

150 ted that the heritability calculated through cross validation is equivalent to trait predictability,

151 In this study Leave One Out (LOO) cross validation is used for validation of our system an

152 cal and super-resolution FM images, where EM cross-validation is not practical.

153 The leave-one-out cross validation (LOOCV) method was implemented to evalu

154 ime domain and the original sparse data, the cross-validation measure is applicable to all reconstruc

155 er 7,000 single RBC images) through a 5-fold cross validation method both for oxygenated and deoxygen

156 e of the method was tested using the 12-fold cross validation method.

157 e crystallographic community in favor of the cross-validation method known as [Formula: see text].

158 action was used to predict genotypic values; cross-validation methods were applied to quantify predic

159 the validity of results, we applied several cross-validation methods.

160 icity calibration values were both 100%, and cross-validation models were performed using FAs and VOC

161 ividual unit, so that standard approaches to cross-validation must be modified.

162 gative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (hig

163 In multivariable analysis with 10-fold cross-validation, NCP burden was the most significant pr

164 Together, our results indicate that cross-validation of Hi-C and FISH should be carefully de

165 concomitantly excluded, proving a functional cross-validation of predictive biomarkers obtained retro

166 many classification methods perform well in cross-validation of single expression profile, the perfo

167 The root mean square error of cross-validation of the model by using external validati

168 A 10-fold cross-validation of the model resulted in a C index of 0

169 drug molecule and its metabolites enabled a cross-validation of the newly developed derivatization p

170 STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutat

171 .57 and an RMSE of 1.09 kcal/mol in a 5-fold cross validation on a set of 223 membrane protein mutati

172 ouring versus orphan pairs; and (iii) k-fold cross validation on experimentally validated datasets.

173 ing jack knife test and 88.87% using 10-fold cross validation on the benchmark dataset.

174 The pipeline was tested using 5-fold cross validations on a comprehensive set of 2,100 non-re

175 The evaluation of our models included cross-validation on specific PBM array designs, testing

176 ing 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n = 387), an

177 iction accuracies were examined by five-fold cross-validation on the genotype-phenotype datasets.

178 When tested by the most rigorous cross-validation on the same high-quality benchmark data

179 disease, with 87% accuracy by leave-one-out cross-validation on training data (N = 23) and 72% accur

180 der the ROC curve was 0.91 (0.80 with 4-fold cross-validation, P = 0.01), indicating a significant pr

181 On cross-validation performance, this detector correctly id

182 ombination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtain

183 orm quantification is reported by performing cross-validation prediction tests with datasets from hum

184 The standard errors for calibration, cross-validation, prediction, and RPD ratios (SD/SECV) w

185 ect classification and prediction by using a cross validation procedure.

186 hly practicable calculating algorithm with a cross-validation procedure are provided to numerically e

187 ed 148 metabolites following a leave-one-out cross-validation procedure or by using MS/MS spectra exp

188 s were calculated using a Monte-Carlo 5-fold cross-validation procedure.

189 showed robust diagnostic classification and cross-validation procedures substantiated these items.

190 ctive ability using logistic regression in a cross-validation process, sensitivity and specificity us

191 ith excellent accuracy using a leave-one-out cross-validation process.

192 idation without replication, and leave-1-out cross-validation produced optimism-adjusted estimates of

193 ermined at a mass-to-charge ratio of 127 for cross-validation purposes.

194 oundwater (222)Rn results in a leave-one out cross-validation r(2) of 0.46 (Pearson correlation coeff

195 erformed well, resulting in an out-of-sample cross-validation R(2) of 0.724.

196 ge fraction (70%) of sites were withheld for cross-validation (R(2) = 0.78) and developed seasonal sk

197 tion accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixe

198 ystems-based spatiotemporal smoothing model (cross-validation R2 = 0.87) that incorporated community-

199 For prediction of impaired MFR with 10-fold cross-validation, receiver operating characteristics are

200 We train (with cross-validation) reference-aided sparse multi-class cla

201 d 98%, for the independent test set and full cross-validation respectively, the method is outperformi

202 t of 83.3% (initial classification) and 81% (cross-validation), respectively.

203 Internal model-cross-validation resulted in a R(2) of 0.57 and a predic

204 Cross-validation results on a dataset of known MPs and n

205 Ten-fold cross-validation revealed a good model performance with

206 Cross-validation revealed good disease-prediction capabi

207 l provided a least root mean square error of cross validation (RMSECV) equal to an acrylamide concent

208 In PLS-R the Root Mean Square Error of Cross Validation (RMSECV) for parasite concentration (0-

209 Root mean square errors of cross validation (RMSECV) were 8.0mg/L and 1.9mg/L for N

210 ration (RMSEC) and root mean square error of cross validation (RMSECV).

211 prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of

212 A cross validation scheme in which 10% of drug-adverse rea

213 ished models were validated in a Monte Carlo cross-validation scheme.

214 tistical models were tested using two random cross-validations schemes.

215 Tenfold cross-validation showed 85.7% sensitivity, 60.7% specifi

216 Cross-validation showed that the spatially structured mo

217 Rigorous cross-validations showed that the proposed predictor ach

218 A 10-fold cross-validation shows that JRFR outperforms other popul

219 n the calibration data set, and 94.4% in the cross-validation step.

220 By applying a leave-one-out cross-validation strategy, we could show that the propos

221 ient's perspective, and will undergo further cross-validation studies in CMT.

222 Congruent with this finding, cross-validation studies indicated that GP including the

223 Through simulation and cross-validation studies, we demonstrated the accuracy o

224 uality of the aggregate and perform in-depth cross-validation studies; (ii) second, we propose a new

225 The developed model was validated using cross-validation technique.

226 ed well with the L-curve and the generalized cross-validation techniques.

227 Results Our study showed that (1) cross-validation tended to underestimate the error rate

228 In a cross-validation test on a benchmark set of 42 proteins,

229 In this paper, five-fold cross-validation test results based on the same benchmar

230 and improved our final prediction by using a cross-validation test.

231 our outcome predictions were estimated with cross-validation (test-fold balanced accuracy [BAC] of 7

232 Rigorous cross-validation tests have indicated that IENHANCER-2L:

233 Cross-validation tests on a SCOP benchmark dataset have

234 Indeed, in cross-validation tests, the covariance matrix estimator

235 We demonstrate via cross-validation that classification and regression appr

236 terns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power.

237 ance of the genetic values predicted through cross validation, the residual variance is the variance

238 the same training data in testing in 3-fold cross-validation, the average recall rate within the top

239 etween the groups were used to predict, with cross-validation, the presence of psychotic symptoms in

240 After optimizing the feature sets via cross-validation, the trained classifiers enable identif

241 a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found t

242 uted from the expert placements with 10-fold cross validation to separate the patients used for train

243 We calculated performance measures with cross-validation to avoid making biased assessments.

244 racy but must be incorporated carefully into cross-validation to avoid overfitting.

245 We then used pattern classification with cross-validation to determine individual patient-level c

246 pared using adjusted R and c-statistics with cross-validation to estimate predictive discrimination.

247 We used feature selection and cross-validation to find 'good' prognostic models for ea

248 was assessed and validated by using 10-fold cross-validation to limit the effect of optimistic bias.

249 used logistic regression with leave-one-out cross-validation to predict outcomes, and incorporated m

250 ity to new patients, we used repeated nested cross-validation to prevent information leaking between

251 nment of arrays to comparison groups allowed cross-validation to provide an unbiased error estimate.

252 We used regularized regression with repeated cross-validation to select from and estimate contributio

253 We use cross-validation to select the model with the best predi

254 regularization, which uses repeated internal cross-validation to select variables and estimate coeffi

255 entally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS part

256 der), with 100% classification accuracy in a cross validation using a leave-one-out technique.

257 his important yet overlooked complication of cross-validation using a unique pair of data sets on the

258 Repeated 10% cross-validation using bootstrap sampling (n = 10,000) d

259 g the spatial organization of brain regions, cross-validation using multiple techniques should be use

260 ity and specificity, respectively, on 5-fold cross-validation using our local data set.

261 Cross-validation using over 17,000 known drug-disease as

262 EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as t

263 by minimization of root-mean-square error of cross-validation values regarding the spectral range, de

264 ing it from white adipose tissue (WAT) using cross-validation via PET.

265 Rigorous cross-validations via a set of multi-label metrics indic

266 f textural features was assessed and 10-fold cross validation was performed.

267 Ten-fold cross validation was used to evaluate prediction accurac

268 Bootstrap cross validation was used to predict the best classifier

269 Internal cross-validation was applied for calibration and predict

270 When cross-validation was employed in the training set of sam

271 s (PLS-DA) with double leave-one-patient-out cross-validation was performed to distinguish tumors fro

272 Cross-validation was performed using a random subset of

273 An internal cross-validation was performed using bootstrap methods.

274 Leave-one-out cross-validation was performed.

275 Ten-fold cross-validation was used for the receiver operating cha

276 Leave-one-out cross-validation was used for validation.

277 cted to maximize the log-rank statistic, and cross-validation was used to obtain unbiased point estim

278 e spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 pre

279 During 10-fold cross validation, we increased the accuracy of the class

280 Through cross-validation, we achieved an accuracy of 97% for the

281 Using neighbor voting in cross-validation, we find that single-cell network conne

282 nformed simulations and simulation-based ABC cross-validation, we first show that neighborhood size c

283 Based on benchmarking experiments with full cross-validation, we show that this predictor generates

284 Logistic regression and 10-fold cross validation were used to derive and validate the co

285 or our models as revealed by the Monte Carlo cross-validation were 0.9 for M36IEP, 0.87 for M36EP, 0.

286 han 0.700) and acceptable standard errors of cross-validation were obtained.

287 ess classifiers using a nested leave-one-out cross-validation were used to predict the treatment resp

288 ded to decline, but remain significant, when cross-validations were performed across subpopulations.

289 gonality of the NMR and MS techniques allows cross-validation, which is especially important to searc

290 traits based on the 839 metabolites through cross-validation, which showed that metabolomic predicti

291 chine, which is conducted in form of 10-fold cross validation with beat-based and record-based traini

292 % accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectiv

293 Seed-based analysis was performed for cross validation with ICA networks by using Pearson corr

294 In the developed method, a cross-validation with 50% of the samples was used for PL

295 and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN.

296 Cross-validation with multiple analytical methods using

297 n dual-labeled with a radionuclide to enable cross-validation with nuclear imaging.

298 hods have markedly improved along with their cross-validation with other computational and experiment

299 We found that sample splitting, cross-validation without replication, and leave-1-out cr

300 initial severity combined with leave-one-out cross-validation yielded a categorical prediction of cli