ライフサイエンスコーパス: validation

1 ge acquisition and processing, and structure validation).

2 abine, although this will require additional validation.

3 tates, and Asia (490 patients), was used for validation.

4 ance with the different guidelines of method validation.

5 te 17-y-old participants using 10-fold cross-validation.

6 described along with details about the model validation.

7 in 21 months (0.97 +/- 0.02 AUCROC) on cross-validation.

8 identify areas of the model requiring deeper validation.

9 An external independent cohort was used for validation.

10 ent assessment metrics, and lack of clinical validation.

11 receptor sequencing and flow cytometry-based validation.

12 must be invested for intralaboratory method validation.

13 A subset of data was reserved for external validation.

14 potential to transform target selection and validation.

15 r radical prostatectomy (RP) using composite validation.

16 ependent Western cohort is used for external validation.

17 fter hospitalization and warrant prospective validation.

18 % confidence interval: 0.791-0.945) in model validation.

19 future prospective analysis is required for validation.

20 athway CNNs was trained using fivefold cross-validation.

21 sol as an important chemical tool for target validation.

22 ndergone successful prospective and external validation.

23 validated confirmed with and fivefold cross-validation.

24 training-validation split and fourfold cross-validation.

25 , and we concentrate here on map quality and validation.

26 tion were set aside for independent external validation.

27 feature extraction, classification and model validation.

28 y of STP dosimetry, with limited independent validations.

29 curacy (92%) when evaluated via strict cross-validations.

30 %) during calibration, but slightly worse in validation (73.5% vs 77.3%) and their combination improv

31 th the training (95% CI 0.903-0.969) and the validation (95% CI 0.888-0.984) datasets for the new pan

32 Concerning LFIA, validation according to the Commission Regulation 519/20

33 der.The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter ru

34 edictions were evaluated using 10-fold cross-validation against annotations by expert surgeons.

35 It is based on k-fold cross-validation algorithm, but in contrast to conventional cr

36 Validation analyses using 54,444 deaths from 7 June to 1

37 currently available methods, according to a validation analysis of a set of protein spectra with Pro

38 For the validation analysis, 10-2 VF results from ADAGES perform

39 ce interval, 77.6%-99.2%) by histopathologic validation and 96.2% (95% confidence interval, 86.3%-99.

40 We describe the development, validation and application of an intersectional approach

41 characteristics is required for further BTMs validation and appropriate PIMP management.

42 the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by

43 d using linear discriminant analysis for the validation and characterization of the surface chemistry

44 , for selecting mapping references, assembly validation and detection of strains of non-human origin.

45 uppressed individuals in the Reservoir Assay Validation and Evaluation Network (RAVEN) study cohort (

46 ional wall motion abnormalities in the cross-validation and external validation datasets with an area

47 Minimal validation and limited access to transparent software pl

48 syndrome had good discrimination in external validation and may be used by other health systems for c

49 We did an in vivo validation and we could show that some of the predicted

50 and its design, safety, efficacy and equity, validation, and liability, as well as how its data were

51 d 10% of examinations allocated to training, validation, and test sets, respectively.

52 using a stepwise discovery, prioritization, validation, and testing in independent cohort design, we

53 using split-sample validation into training, validation, and testing sets, respectively.

54 bolites and applying the leave-one-out cross-validation approach.

55 from raw images were used for training, the validation area under the curve significantly decreased

56 tered list of genes for further experimental validation as well as several accompanying data visualis

57 tified modules, we also performed literature validation as well as validation using experimentally su

58 Subsequent validation assays using resynthesized compounds, several

59 u assembly correctly resolves 337 out of 341 validation BACs sampled from known segmental duplication

60 Validation based on data from May and June 2020 confirms

61 esults provides an additional level of model validation based on expert knowledge.

62 del requires further refinement and external validation, but it may hold promise for HCM screening.

63 ed in beta cell proliferation for subsequent validation by RT-PCR.

64 ion, as well as disease modeling and in vivo validation capabilities.

65 in a separate mouse cohort, as well as early validation clinical data consisting of patients receivin

66 HCC status among at-risk individuals in the validation cohort (area under the curve: 0.91 [95% CI 0.

67 e identified a second variant in FAF1 in the validation cohort (c.254G>C; p.Arg85Pro).

68 In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88.

69 In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but

70 ith a training cohort (n = 40) and a blinded validation cohort (n = 90) acquired from CDC, the Lyme I

71 th the highest level of specificity (100% in validation cohort 1 and 87.8% in validation cohort 2), w

72 biologic-naive patients with early-stage CD (validation cohort 1) and 195 biologic-exposed patients w

73 ty (100% in validation cohort 1 and 87.8% in validation cohort 2), with sensitivity values of 37.3% a

74 5 biologic-exposed patients with chronic CD (validation cohort 2).

75 erating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI),

76 dard histologic assessment in an independent validation cohort of 56 additional cases.

77 , alongside 13 722 UK Biobank controls and a validation cohort of 92 SCAD survivors.

78 nfection (n = 25), followed by a multicenter validation cohort of allograft BKVN (n = 60) vs TCMR (n

79 the detection of seroconversion in a blinded validation cohort of samples collected before the pandem

80 The Danish validation cohort, confirmed our findings regarding the

81 In the validation cohort, inflammation was upregulated in HFpEF

82 In the validation cohort, models for intubated pediatric acute

83 Within the validation cohort, no significant differences in index b

84 In the external validation cohort, the machine learning model identified

85 ss index, and type-2 diabetes in the phase 2 validation cohort, the minor A allele of MARC1:rs2642438

86 TB cases from non-TB disease controls of the validation cohort, which demonstrated better discriminat

87 h an AUROC of 0.96 in a large, international validation cohort.

88 UC of 0.78, correctly classifying 83% of the validation cohort.

89 ese findings were corroborated in a separate validation cohort.

90 ent in HFpEF cases (but not controls) in the validation cohort.

91 ll subtypes treated from 2005 to 2014 as the validation cohort.

92 an AUC of 0.798 (95% CI 0.789-0.818) in the validation cohort.

93 Findings were confirmed in a validation cohort.

94 e in convalescents was confirmed in a larger validation cohort.

95 esponse in CLL successfully in discovery and validation cohorts and, in day 28 samples, reported resp

96 ce of obstructive CAD more accurately in the validation cohorts than the PTP model, and markedly incr

97 erating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [

98 n into derivation, and internal and external validation cohorts, and used five feature selection tech

99 easibility study in distinct development and validation cohorts, involving retrospective analysis of

100 In the test and validation cohorts, patients with SBP and higher concent

101 in the derivation, and internal and external validation cohorts, respectively.

102 nged from 0.84 to 0.88 across derivation and validation cohorts.

103 ent of 99.6% and 97.1%, respectively, in the validation cohorts.

104 and divided the dataset into derivation and validation cohorts.

105 C, 0.94; 95% CI, 0.92-0.95) were used as the validation cohorts.

106 y; four studies used traditional replication/validation cohorts.

107 partitioned temporally into development and validation cohorts: the logistic regression and gradient

108 These findings, upon larger-scale validation, could facilitate the implementation of a per

109 in PET/CT acquisitions (training CTs: 8,632, validation CTs: 53).

110 diabetes (AUC 0.92) based on standard cross-validation (CV).

111 In the validation data set, we also compared our models with an

112 Results are reported for validation data sets only.

113 The cohort was divided into training and validation data sets.

114 In validation data, HW-TMB was associated with survival (p

115 al trials, and real-world population data as validation data.

116 ysis methods when using our CRISPR screen as validation data.

117 results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects accord

118 he method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical

119 he remaining 171 patients and in an external validation dataset of 31 patients based on the analysis

120 ies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men.

121 sted the performance of TT measurements in a validation dataset of retinal detachment images.

122 Validation dataset showed that TT can detect significant

123 eensland cohorts formed a training and cross-validation dataset used to identify structural connectiv

124 In the validation dataset, the receiver operating characteristi

125 In the external validation dataset, the sensitivity was 81.8% (95% CI, 7

126 very dataset; 1.10, 0.98-1.20; p=0.10 in the validation dataset; and 1.11, 1.02-1.22; p=0.018 in the

127 rious effort was tested using derivation and validation datasets using esophageal pressure-time produ

128 alities in the cross-validation and external validation datasets with an area under the receiver oper

129 ease their reliability when used on external validation datasets.

130 Method validation demonstrates good technical precision (CV = 7

131 standard nCV, Elastic Net optimized by cross-validation, differential privacy and private evaporative

132 In internal validation, EasyCIE-SSI had a sensitivity, specificity,

133 In external validation, EasyCIE-SSI had sensitivity, specificity, AU

134 Final validation employed M. tuberculosis-positive clinical sa

135 hyperparameters optimised to minimise cross validation error, ten methods of automated variable sele

136 Initial validation experiments employed mycobacteria DNA, either

137 Validation experiments revealed an increase of PCSK6 on

138 We used blood fructose as a validation exposure.

139 Here, we report our internal validation for aztreonam-avibactam AST by reference brot

140 T2D-associated loci and provide experimental validation for one such signal.

141 Therefore, drug identification and validation for the treatment of sepsis is of the utmost

142 ritical neuroimaging algorithms, as having a validation framework helps (1) developers to build new s

143 External validation generated a C-statistic of 75.0% (95% CI, 73.

144 ubjects in the training group and 105 in the validation group, exosomal alpha-synuclein exhibited a c

145 ug Administration (FDA) Bioanalytical Method Validation Guidance for Industry to evaluate the quality

146 enes associate with hypertension, yet target validation has been negative.

147 stablished for a few bacterial pathogens and validation has not been done in this patient population.

148 rom this unbiased screen and the mechanistic validation highlight Golgi function as one of the key ce

149 This is the first evidence of validation in a Brazilian population of genetic markers

150 =2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Di

151 uite repeatable and can be used for clinical validation in future treatment response assessment studi

152 ne-independent HTT specific immunoassays for validation in human HD and control fibroblasts and use t

153 CA2-deficient mouse embryonic stem cells and validation in KB2P1.21 mouse mammary tumor cells.

154 atients with IBD and the importance of their validation in large, independent cohorts before clinical

155 With validation in larger cohorts, SHIFT could serve as an ef

156 Validation in lung cancer screening trials and not a cli

157 iling, molecular pathway identification, and validation in vitro.

158 37), and 10% (n = 437) by using split-sample validation into training, validation, and testing sets,

159 However, real-world clinical validation is currently lacking.

160 Validation is performed on CGM data of 148 subjects with

161 ics in T1D pathogenesis; however, functional validation is warranted.

162 n of a minimum norm solution optimizes cross-validation leave-one-out stability and thereby the expec

163 egression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature eliminat

164 For validation, lung wedge biopsies were taken from nonobstr

165 ing pathogenic variants and their functional validation may help overcome these caveats allowing for

166 ied into 100 iterations of Monte-Carlo cross validation (MCCV).

167 After validation, microcalcifications found in benign and mali

168 We created discovery (n = 2405) and validation (n = 2313) cohorts using data from 4 recent t

169 randomly split into training (n = 1070) and validation (n = 355) cohorts.

170 atasets were split into training (n=17 340), validation (n=3491), and testing (n=2852) groups, which

171 Nested cross-validation (nCV) is a common approach that chooses the c

172 In this work, the screening and validation of a fragment library are described, and the

173 We performed an extensive validation of all deletions in the significant set of vo

174 This work reports the development and validation of an algorithm for prediction of post-trauma

175 In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therap

176 ogy has largely focused on the discovery and validation of biomarkers, but the systematic analysis of

177 s the need grows for robust verification and validation of candidate quantum processors, since it pla

178 y tool (the IgG4-RD Responder Index) and the validation of classification criteria, both of which wer

179 hown, among others, that the development and validation of computational methods for RNA fold predict

180 ing the following areas: center eligibility, validation of databases, patient cohort selection, proce

181 ec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny gra

182 RNP-MaP enables discovery and efficient validation of functional protein interaction networks on

183 red to wild-type HSPCs, and we provide early validation of G6B as a potential immunotherapy target.

184 In addition, functional validation of genes is indicated based on phenotypes of

185 contrast, the recent discovery and clinical validation of highly potent selective RET inhibitors (pr

186 concurrently with fMRI to provide convergent validation of induced affect processing in the dimension

187 versatility of the compiler and enables the validation of its functionality without physical experim

188 led the robust identification and orthogonal validation of N-glycosylation sites based on alternating

189 Validation of noninvasive biomarkers for monitoring trea

190 ghly permissive SupT1 cells, followed by the validation of our observations in primary CD4 T cells wi

191 Our results include validation of previously reported effects in xenobiotic

192 The development and validation of reliable prognostic models is a recognised

193 Validation of selected genes on pistil tissue of the 26

194 Functional validation of SNPs was carried out using literature.

195 ferently to given activation stimuli, proper validation of suitable reference genes in these subsets

196 During the prospective validation of the derived algorithm (n = 687), HemosIL-A

197 The validation of the method was evaluated using standard re

198 External validation of the model in the SJLIFE cohort produced an

199 o perform CT-guided biopsies with histologic validation of the nonresponding lesions in 7 of these no

200 Evaluation and validation of the outcomes associated with implementing

201 Validation of the predictive model was conducted in a fo

202 First, the validation of the presented numerical platform against e

203 Experimental validation of the proposed model was carried out in the

204 In addition, we demonstrate a method for validation of the segmental size of polymer chains with

205 Future work should include the validation of these biomarkers in a large patient cohort

206 For the validation of this approach and in order to close a gap

207 ealthcare system and then conducted internal validation on a blind cohort from the same healthcare sy

208 system (1850 operative events) and external validation on a blind cohort from the second healthcare

209 Validation on real and simulated data sets shows that MP

210 comes were observed in extensive independent validations on multiple cancer patient datasets obtained

211 t we split into eleven subsets (10 for cross-validation, one for testing) using a novel clustering me

212 rithm, but in contrast to conventional cross-validation, our approach makes it possible to create a n

213 Furthermore, very good validation parameters were achieved (precision and accur

214 s multicenter observational study identified validation patients, who had been eligible but not rando

215 In the final validation phase (phase III), most of the 169 standardiz

216 odel maintained excellent performance in the validation population: AUROC 0.95, CS 1.22, BS 0.05, and

217 ability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range o

218 sampling uncertainty estimated by the cross-validation procedure.

219 After multiple internal and external validation procedures, modules of selected interest were

220 establish successful biomarker discovery and validation programmes.

221 ody measurements are supported by a two-step validation protocol: with the biosensor operating off- a

222 For validation purposes, we also aimed to evaluate these mar

223 We compare validation results obtained using non-sibling subjects t

224 The validation results were within specified limits and the

225 The instrumental methods yielded similar validation results, better than SENS, and their combinat

226 Further analysis and in vitro validation revealed that metformin optimally reverts dia

227 , a systematic evaluation using nested cross-validation revealed that the RILP algorithm selected few

228 Functional validation revealed these dGBMs conveyed synergistic act

229 Validation runs show that the model can satisfactorily d

230 c categories using a 5 x 5-fold nested cross-validation scheme and demonstrated their generalizabilit

231 t the cohort data into a training set (80%), validation set (10%), and test set (10%) - stratified fo

232 in the calibration set and 9 samples in the validation set (4 normal, 2 cancer, 3 precancerous).

233 were divided into a training (n = 318) and a validation set (n = 189).

234 Overall classification accuracy of the validation set (n = 300) was 99.3% (98.3%-100%).

235 uracy of the model was tested on an external validation set from another institution and on a dataset

236 to 2017: a training set (n=886), an internal validation set from site no.

237 of the classification models on an external validation set reveal high accuracy with areas under ROC

238 nd a specificity of 92% in a randomly chosen validation set, a level far superior to established diag

239 % in the training set, 66.7% vs 60.0% in the validation set, respectively).

240 Another cohort (n = 231) served as the validation set.

241 f 92.6% in the training set and 78.5% in the validation set.

242 nship to correct subsequent inference in the validation set.

243 (26 of 26 masses; 95% CI: 87%, 100%) in the validation set; and 83% (24 of 29 masses; 95% CI: 65%, 9

244 to identify EGFR-mutant patients in multiple validation sets with globally good accuracy, especially

245 ues of 0.92 and 0.90 for the calibration and validation sets, and a residual prediction deviation val

246 00) in the training (50% of the dataset) and validation sets, respectively, to 0.936 in both the trai

247 f at least 0.84 and 0.72 in the training and validation sets, respectively.

248 data are divided into training, testing, and validation sets.

249 Results External validation showed excellent correlation (R = 0.99) and e

250 ofiles within the sample, and hold-out cross-validation showed that these profiles were significantly

251 is a large study on LNM in T1 CRC, including validation, showing that LVI and perineural invasion, mu

252 d to train the model with a 75%-25% training-validation split and fourfold cross-validation.

253 thms, was evaluated by internal 5-fold cross-validation statistics.

254 Validation studies in primary human hepatocytes identifi

255 ring the present study may be used in future validation studies involving RNAi/overexpression approac

256 ng to QOL in children with CMT and conducted validation studies to develop a pediatric CMT-specific Q

257 echanistic inference by directing functional validation studies to the most relevant tissues and can

258 e expression inferred from RNA-seq data; (v) validation studies using qRT-PCR were conducted on 26 se

259 Before survey use, the assay underwent four validation studies with pooled estimates of sensitivity

260 Following the validation studies, recovery and relative standard devia

261 This multicenter validation study compared the prognostication of the max

262 For vicinal-diketones group, a complete validation study for the optimal conditions is presented

263 the frame of an official control program the validation study of a molecular test for detection of se

264 This is considered a pre-validation study that has investigated stains and finger

265 Here we report a multicenter "modern-era" validation study that included 99 patients with definiti

266 Next, we carried out a validation study using paired DBS-whole blood samples fr

267 precision of 0.936 and recall of 0.943 on 18 validation subjects, and its performance is on par with

268 Out-of-sample validation suggests that the proposed Bayesian method, w

269 Five-fold cross-validation summaries out to 1000 single-nucleotide polym

270 Functional validation supported a role for glutamate metabolism and

271 is of the master regulator genes followed by validation testing in independent children with severe a

272 d 4) systematic independent replications and validations that integrate different units of analyses a

273 While waiting for further external validation, the CLIV Score based on clinical and immune-

274 During external validation, the n-gram model demonstrated good discrimin

275 At cross-validation, the newly developed SYNTAX score II, termed

276 In external validation, the RSClin risk estimate was prognostic for

277 e element method simulation and experimental validation to demonstrate that the fiber patterning stra

278 ng models include nested leave-one-out cross-validation to select features, train the model, and esti

279 ublished approaches, we offer diagnostic and validation tools for all relevant steps.

280 By using leave-one-out cross validation, two quantitative US multivariable models wer

281 ng use of internal datasets without external validation, unavailability of implementation methods, in

282 o factors are evaluated with an internal BLP validation using a calibration set.

283 crucial hotspot inter-atomic interactions to validation using data on site-directed mutagenesis' effe

284 External validation using EMTAB-365 dataset showed similar observ

285 o performed literature validation as well as validation using experimentally supported miRTarBase dat

286 with sleep duration observationally and with validation using Mendelian Randomization.

287 nts in the population for in vivo functional validation, using computational, in vitro and in vivo ap

288 Closed-loop validation, using stimuli lying in the null space of the

289 19 (COVID-19) pandemic, design, development, validation, verification and implementation of diagnosti

290 de with the AJCC staging system and external validation was performed in an independent cohort with p

291 External validation was performed with 32 patients treated at a s

292 c regression analysis with leave-1-out cross validation, we developed a model, including a viral dive

293 ng computational simulation and experimental validation, we show that a major contributor to this slo

294 om different cohorts, new data and synthetic validation, we show that this method is more robust and

295 ic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs.

296 nal lengthy, culture-based surface sterility validation, which is critical in hospitals, food and pha

297 an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of

298 aracterization of prostate cancer (PCa), but validation with histopathology is lacking.

299 Statistical validation within the workflow included repeated runs of

300 Further validation work is critical to unleashing the full poten