ライフサイエンスコーパス: ROC

1 ROC analyses were carried out, cut-offs selected using a

2 ROC analysis confirmed the optimal threshold and generat

3 ROC analysis indicated that H1 concentration is potentia

4 ROC analysis revealed 4 grades of total severity score o

5 ROC analysis revealed that CTRP7 and CTRP15 may serve as

6 ROC analysis reveals that the count ratio of immature ne

7 ROC analysis showed that cathepsin Z mRNA has strong dia

8 ROC analysis was performed to determine the optimal rCBV

9 ROC analysis yielded an optimal cut-off value of 2.245 (

10 ROC analysis yielded diaschisis thresholds of 0.62 for T

11 ROC and AUC risk score analysis suggested that MSA4A (AU

12 ROC curve (AUROC) analyses compared the diagnostic accur

13 ROC curve analysis at 120 days after ingestion showed th

14 ROC curve analysis determined an ADC value of 958 x 10(-

15 ROC curve analysis indicated that the optimal cutoff poi

16 ROC curve analysis revealed the current impedance standa

17 ROC curve demonstrated that studies receiving greater th

18 ROC curve: the SI cutoff was 0.52 and 0.33 respectively

19 ROC curves constructed to determine optimal size and att

20 ROC curves of Cho SNR showed statistically significant d

21 ROC evaluation of the method on in silico data showed hi

22 ROC operates in various subcellular fractions including

23 ROCs are present in the epidermis during normal tail dev

24 hest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by lea

25 control samples (Area under the curve 0.851, ROC-analysis).

26 A ROC curve analysis was performed in the derivation cohor

27 ayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation.

28 best performer with an accuracy of 90% and a ROC-AUC of 0.96.

29 tilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).

30 ine) was enhanced by ratio with CSF Abeta42 (ROC > 0.8), and spermidine significantly correlated with

31 w score, had equivalent diagnostic accuracy (ROC difference: P = .24).

32 74-0.92]), but not alanine aminotransferase (ROC AUC 0.46 [0.35-0.57]).

33 inal nerve fiber layer predictions showed an ROC curve area of 0.86 (95% CI, 0.83-0.88) to discrimina

34 dict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85

35 Receiver Operative Characteristics analysis (ROC) respectively.

36 e model performed very well (TSS = 0.898 and ROC = 0.991) and indicated high environmental suitabilit

37 set and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dat

38 SNR) measured followed by histopathology and ROC analyses.

39 ICP was 5 mmHg; obtained when ICA volume and ROC were 20 ml and 1,600 ft/min, respectively.

40 P, was found to increase with ICA volume and ROC.

41 high-risk from lower-risk coronary arteries (ROC AUC: 0.76; 95% CI: 0.62 to 0.91; p = 0.002); however

42 At ROC-defined thresholds, APRI (>/=0.33), AAR (>/=0.93), F

43 e of receiver operating characteristics (AUC ROC) (0.978).

44 updated PELD-Na-Cr had a cross-validated AUC ROC of 0.854, vs 0.799 for the original PELD.

45 r liver transplantation score obtains an AUC-ROC of 0.638 (95% CI, 0.632-0.645).

46 end-stage liver disease score yields an AUC-ROC of 0.764 (95% CI, 0.756-0.771), whereas survival out

47 curately ranked the reference compounds (AUC-ROC >= 0.9).

48 receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for

49 ) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of

50 The AUC-ROC values of IP-10, IL-6, and both biomarkers combined

51 0.07), and performed well as CSF biomarkers (ROC > 0.75).

52 e of the strain ratio (4.25) was obtained by ROC curve analysis, the sensitivity and specificity for

53 the central role of reactive organic carbon (ROC) in the formation of secondary species that impact g

54 ch we term the regeneration-organizing cell (ROC).

55 ned using receiver operating characteristic (ROC) analyses and compared with that of CSF core biomark

56 riates in receiver operating characteristic (ROC) analyses.

57 Receiver operating characteristic (ROC) analysis along with the calibration test was used t

58 Using receiver operating characteristic (ROC) analysis and adjusting the cutoff levels, we improv

59 Receiver operating characteristic (ROC) analysis demonstrated that this integrated EV doubl

60 Receiver operating characteristic (ROC) analysis of test samples showed sensitivity/specifi

61 NOVA) and receiver operating characteristic (ROC) analysis revealed that the peak height ratios were

62 Receiver operating characteristic (ROC) analysis revealed the best predictability for APO w

63 We used receiver operating characteristic (ROC) analysis to evaluate the discriminative ability of

64 Receiver operating characteristic (ROC) analysis was performed to compare the accuracy of t

65 Receiver operating characteristic (ROC) analysis was performed using the average score for

66 Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accur

67 Based on receiver operating characteristic (ROC) analysis, a model value > - 0.19 was selected as th

68 Receiver operating characteristic (ROC) analysis, odds ratios and binary logistic regressio

69 ated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) a

70 mputed by receiver operating characteristic (ROC) analysis.

71 ted using receiver operating characteristic (ROC) analysis.

72 ded using receiver operating characteristic (ROC) analysis.

73 -qPCR and receiver operating characteristic (ROC) analysis.

74 odels and receiver operating characteristic (ROC) analysis.

75 by using receiver operating characteristic (ROC) and chi(2) analysis.

76 ets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves.

77 l summary receiver operating characteristic (ROC) and the bivariate logit-normal (Reitsma) models.

78 n, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 +/- 0.006 for i

79 Under the receiver operating characteristic (ROC) Curve (AUC) can be improved from around 0.60 using

80 under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved

81 under the receiver operating characteristic (ROC) curve (AUC) of 1.00, 0.75 and 0.73, respectively.

82 under the receiver operating characteristic (ROC) curve (AUC) was calculated for transverse shear-wav

83 under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statis

84 under the receiver operating characteristic (ROC) curve (AUC); the Mean Reciprocal Ranking (MRR) of p

85 Receiver operating characteristic (ROC) curve analysis demonstrated a CT-score cut-off of 1

86 Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discri

87 Using receiver operating characteristic (ROC) curve analysis, optimal cut-off values for left ven

88 ined with receiver operating characteristic (ROC) curve analysis.

89 ng by the receiver operating characteristic (ROC) curve analysis.

90 using the receiver operating characteristic (ROC) curve and compared using the DeLong test.

91 under the receiver operating characteristic (ROC) curve and the sensitivity with fixed specificities

92 under the receiver operating characteristic (ROC) curve for the classifiers was 0.973 with a sensitiv

93 under the receiver operating characteristic (ROC) curve of 0.75 +/- 0.1, 0.80 +/- 0.1 and 0.89 +/- 0.

94 under the receiver operating characteristic (ROC) curve of 0.8 compared to 0.73 with the AHP based mo

95 under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98).

96 under the receiver operating characteristic (ROC) curve of 0.93 on 10-fold cross-validation.

97 ploys the receiver operating characteristic (ROC) curve to minimize false discovery rate (FDR) and ca

98 under the receiver operating characteristic (ROC) curve was 0.95.

99 under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when

100 A receiver operating characteristic (ROC) curve was carried out to identify the cutoff for co

101 Receiver operating characteristic (ROC) curve was drawn to determine a cut-off ADC value fo

102 nerated a receiver operating characteristic (ROC) curve, and calculated area under the curve (AUC) an

103 under the receiver operating characteristic (ROC) curve.

104 under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confid

105 Receiver operating characteristic (ROC) curves analysis and Area Under the Curve (AUC) esti

106 using the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics.

107 terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model sim

108 generated receiver operating characteristic (ROC) curves to evaluate the optimal ZD cutoff criteria.

109 We used receiver operating characteristic (ROC) curves to examine the prognostic accuracy of the in

110 Receiver operating characteristic (ROC) curves were generated to determine area under the c

111 of TMJOA, receiver operating characteristic (ROC) curves were plotted.

112 Receiver-operating characteristic (ROC) curves were then calculated to determine the optimu

113 iance and receiver operating characteristic (ROC) curves were used to determine the significance of e

114 ion using receiver operating characteristic (ROC) curves, calibration using Cox linear logistic regre

115 Receiver operating characteristic (ROC) curves, category-free net reclassification index (c

116 Using receiver operating characteristic (ROC) curves, we determined the optimal cut-off values fo

117 ate model receiver operating characteristic (ROC) curves.

118 under the receiver operating characteristic (ROC) curves.

119 ted using receiver operating characteristic (ROC) curves.

120 red using receiver operating characteristic (ROC) curves.

121 sessed by receiver operating characteristic (ROC) curves.

122 ted using Receiver Operating Characteristic (ROC) curves.

123 ulated by receiver operating characteristic (ROC) curves.

124 luated by receiver operating characteristic (ROC) curves.

125 cificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbo

126 red using receiver-operating-characteristic (ROC) curve analysis and Kaplan-Meier survival curves.

127 (SUVRs), receiver-operating-characteristic (ROC) curves based on clinical classification of cognitiv

128 Receiver operation characteristic (ROC) curve analysis showed that the trifucosylated N-gly

129 ved using receiver operative characteristic (ROC) curves.

130 under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00])

131 Receiver operator characteristic (ROC) analysis for distinguishing periodontitis from heal

132 mined, and receiver operator characteristic (ROC) analysis was used to identify an optimal threshold.

133 under the receiver-operator characteristic (ROC) curve (AUC).

134 Receiver operator characteristic (ROC) curve analysis showed that IL17A can be used to dis

135 Receiver operator characteristic (ROC) curve analysis was used to assess the diagnostic po

136 Summary receiver operator characteristic (ROC) curve demonstrated superior accuracy of direct MR a

137 under the receiver operator characteristic (ROC) curve of 0.8.

138 by using a receiver operator characteristic (ROC) curve.

139 under the receiver operator characteristic (ROC) curves (Az value), specificity, sensitivity, positi

140 parison of receiver-operator characteristic (ROC) curves and Decision Curve Analysis (DCA).

141 generated receiver-operator characteristic (ROC) curves for the full models overlaid with Index as a

142 modeling, receiver-operator characteristic (ROC) curves, and calibration plots.

143 sing their Receiver Operator Characteristic (ROC) curves; Area Under Curve (AUC) and accuracy were ca

144 riate and receiving operator characteristic (ROC) methods.

145 using the receiver-operator-characteristic (ROC).

146 under the receiver operating characteristic [ROC] curve [AUROC] for wPRx was 0.73 versus 0.66 for PRx

147 response (receiver operating characteristic [ROC] curve, area under the curve [AUC] = 0.82, P = 0.02)

148 ed using receiver operating characteristics (ROC) and decision curve methodology against histologic E

149 ver, the receiver operating characteristics (ROC) curve analyses of miR-630 and miR-378g yielded area

150 AUCs) on receiver operating characteristics (ROC) curves as performance measures, the models did comp

151 set, the receiver operating characteristics (ROC) were compared between the models trained in the cen

152 te serial receiver operator characteristics (ROC) curves to assess the sensitivity and specificity of

153 tidomain protein with both a Ras of complex (ROC) domain and a kinase domain (KD) and, therefore, exh

154 ifunctional retinoid oxidoreductive complex (ROC).

155 dexmedetomidine and return of consciousness (ROC) in a functionally interconnecting somatosensory and

156 from the Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (enrollment, April 2

157 the receiver operating characteristic curve (ROC) of 0.97.

158 the receiver operating characteristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibit

159 the receiver operating characteristic curve (ROC-AUC) of 0.799 using random cross-validation, and 0.7

160 amount received by receiver operating curve (ROC).

161 accuracy of 66% on Receiver Operator Curve (ROC) analysis to predict for successful SWL outcome.

162 erating characteristic area under the curve (ROC-AUC) >= 0.75 in high-myopia subjects compared to con

163 the receiver operating characteristic curve [ROC AUC]) 0.83 [95% CI 0.74-0.92]), but not alanine amin

164 by receiver operator characteristic curves (ROC-AUCs).

165 Receiver operator curves (ROC) were constructed at an independent image level, mac

166 The receiver operating curves (ROCs) were plotted for every measurement and contrasted

167 d elderly versus Alzheimer disease dementia, ROC curves based on visual Abeta-positive/Abeta-negative

168 Indians but slightly less than in Europeans (ROC AUC 0.84 v 0.87, p < 0.0001).

169 Models had excellent (ROC-AUC > 0.9) prediction of response in pre-clinical da

170 We then used these lead candidates for ROC analysis and found multiple biomarkers with values a

171 ed dichotomously with optimized cutoffs from ROC analyses, we achieved 99.5% concordance with IHC; an

172 lassification), no significant difference in ROC was found between centralized and distributed models

173 No difference in ROC was observed with respect to feature selection.

174 onths prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of

175 When each sign is considered independently, (ROC analysis, followed by binary logistic regression) on

176 ndent of the concentration of the individual ROC components.

177 for gangrenous cholecystitis with AUC of its ROC as 0.92 (95% CI: 0.80-1.00, p = 0.001) with an ideal

178 LOC, ROC and full task performance recovery were all associat

179 human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DB

180 We find that average measured ROC concentrations are about twice as high in Pasadena (

181 gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-A

182 served good convergence among the 4 methods: ROC curves based on visual classification (optimal cut p

183 superior performance to the baseline model (ROC-AUC = 0.705).

184 C: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62).

185 models outperformed beta-value based models (ROC-AUC 0.81 +/- 0.01 vs. 0.73 +/- 0.02, mean +/- SEM, c

186 Moreover, ROC(N1437H) was found to have a slower GTP dissociation

187 The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform

188 Analysis of ROC curves showed that high-intensity long-run emphasis

189 l air quality and climate, our assessment of ROC abundance and impacts is challenged by the diversity

190 These features of ROC ensure that the rate of RA biosynthesis in whole cel

191 0 and DHRS3, suggesting reduced formation of ROC.

192 We revisit measurements of ROC species made during two field campaigns in the Unite

193 cks regeneration, whereas transplantation of ROC-containing grafts induces ectopic outgrowths in earl

194 Genetic ablation or manual removal of ROCs blocks regeneration, whereas transplantation of ROC

195 n the GTPase domain Ras of complex proteins (ROC) of leucine rich repeat kinase 2 (LRRK2) result in a

196 In contrast with mutations at R1441, ROC(N1437H) was found to be locked in a stable dimeric c

197 ylaxis and cardiovascular/febrile reactions, ROC curve analysis revealed a reasonably high area under

198 The area under the ROC (ROC-AUC) when using these seven predictors was 0.88.

199 s then validated on 158 independent samples (ROC-AUC = 0.825).

200 62 K. pneumoniae and 348 E. faecium samples, ROC curves demonstrate that the conserved-sequence genom

201 FTND and NCC in discerning disease severity (ROC curve analysis: AUC = 0.746, P = 0.027) was superior

202 Significant ROC and positive correlation with Ki67 index highlight t

203 Summary ROC curve demonstrated overall superiority of 3-T imagin

204 LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area unde

205 ctively; the area under the curve in summary ROC was 0.87 (95% CI: 0.84-0.90).

206 assification (optimal cut point, 1.55 SUVR), ROC curves based on clinical classification (optimal cut

207 We show that ROC is composed of at least two subunits of NAD(+)-depen

208 Transcriptional profiling revealed that ROCs secrete ligands associated with key regenerative pa

209 The ROC analysis produced an area under the curve of 0.87, i

210 The ROC curve decided that the cutoff point of FENO was 37.8

211 The ROC curve showed the optimum cut-off point at 0.628 x 10

212 The ROC curve was drawn to differentiate grade II from grade

213 The ROC curve was significant for SEVs concentration to pred

214 chniques were used to compare groups and the ROC curve to evaluate classification algorithms.

215 the results for the commercial ELISA, as the ROC analysis of the GPI1 test shows 97% specificity and

216 y of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the da

217 en both THGr(Ce) and THGr(Cb) were below the ROC threshold, the combined diaschisis measures had a po

218 nt difference within the methods between the ROC curves (P > 0.4) for progression-free survival and o

219 In the forme fruste eyes, the ROC analysis showed that the AUC values of the mean K, t

220 There were no significant differences in the ROC area under the curve or on decision curve analysis.

221 inciple produce 50 positive instances in the ROC curves.

222 ation impairs GTPase activity by locking the ROC domain in a persistently dimeric state.

223 AUC of the ROC curve was 0.999.

224 On the ROC analysis, APPLE (AUC 0.640, 95%CI 0.602-0.677, p < 0

225 city based on optimal operating point on the ROC curve (7.0 ng/mL) were both 93%.

226 This analysis suggests that the ROC in urban California is less reactive, but due to hig

227 According to the ROC curve, the learning curve was completed after 25 pro

228 The area under the ROC (ROC-AUC) when using these seven predictors was 0.88

229 racy than CAP in terms of the area under the ROC curve (0.99 vs 0.77, respectively; P = .0334).

230 ms, the Model 2 showed higher area under the ROC curve (82.2%, 95% CI 79.6%-84.7%) and good calibrati

231 In the training period, the area under the ROC curve (aROC) of airborne particle counting and EBC w

232 -fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) m

233 stic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of dist

234 The primary outcome was the area under the ROC curve (AUC) for GFAP in patients with CT-negative an

235 ur classifiers achieved 72.2% Area Under the ROC Curve (AUC) for predicting carcinogenicity and 82.3%

236 The area under the ROC curve (AUC) for the assessment of group 1 was 0.78 (

237 The area under the ROC curve (AUC) is often used to evaluate the performanc

238 reached an optimism-corrected area under the ROC curve (AUC) of 0.86.

239 n, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better th

240 formance was scored using the Area Under the ROC Curve (AUC) statistic.

241 in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76.

242 The highest area under the ROC curve (AUC) values were obtained at 200 Hz and range

243 The area under the ROC curve (AUROC) was 0.98 for peanut, 0.97 for cashew,

244 ound between their respective area under the ROC curve (p=0.0650).

245 lidated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background pr

246 and high diagnostic accuracy (area under the ROC curve = 0.95; 95% confidence interval [CI]: 0.93, 0.

247 The MUAC area under the ROC curve accuracy level in identifying severe wasting w

248 ate-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer.

249 Areas under the ROC curve for the full models were 0.69 (95% CI = 0.67 -

250 tes alone, which exhibited an area under the ROC curve of 0.57 (p < 0.008).

251 rence prediction model had an area under the ROC curve of 0.786.

252 and atypical bacteria with an area under the ROC curve of 0.79 (95% CI, .75-.82).

253 ns) can identify SPEs with an area under the ROC curve of 0.89.

254 del classifies images with an area under the ROC curve of 0.897, and a sensitivity of 0.783 and speci

255 The DL algorithm had an area under the ROC curve of 0.979 (95% CI: 0.974 to 0.984) and an overa

256 esults The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00)

257 omarker for MDD, achieving an area under the ROC curve of 0.999 in discriminating drug-naive MDD pati

258 miR31 and FEM1C presented an area under the ROC curve of 96.7%, followed by SENP1 with 93.3%.

259 The area under the ROC curve of the algorithm for each corneal disease type

260 The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European

261 Finally, miR95 had an area under the ROC curve value <86.7%.

262 The accuracy and area under the ROC curve, AUC, was 88.9% and 0.94, respectively.

263 -art gkmSVM-2.0 algorithms in area under the ROC curve, while achieving average speedups in kernel co

264 models tested yielded ~72% of area under the ROC curve.

265 9.9% (95% CI 53.7%-75.5%) and area under the ROC curve= 76.0% (95% CI 56.8%-82.1%).

266 sequence classifiers yielded areas under the ROC curves (AUCs) [95% confidence intervals] of AUC(DCE)

267 The area under the ROC curves for discriminating glaucomatous from healthy

268 miR-630 and miR-378g yielded areas under the ROC curves of 0.82 (95% CI 0.67 to 0.82) and 0.83 (95% C

269 The areas under the ROC curves were 0.89 (95% CI: 0.84-0.95) for global RNFL

270 The area under the ROC for using small-window entropy imaging to classify t

271 n outpatients and others with area under the ROC of 0.80 (95% CI 0.68-0.92).

272 macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%.

273 patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%.

274 gressors (slope faster than 2 mum/year), the ROC curve area was 0.96 (95% CI, 0.94-0.98), with a sens

275 and, finally, a global cut-off point through ROC and precision-recall analysis in a voice disordered

276 According to ROC curves, in the tumor group, at the cut-off value of

277 ll previous ones in terms of both area under ROC and precision-recall curves in standard benchmark te

278 ly BMOBRW parameter that achieved area under ROC curve >=0.80 was infero-temporal (0.82).

279 Area under ROC curve (AUC) was used as diagnostic performance crite

280 ILI after acetaminophen overdose (area under ROC curve 0.98 (95% CI; 0.96-1), P < 0.0001).

281 ariables in a given model and the area under ROC curve was calculated.

282 The area under ROC from three-dimensional combination of PGI/II-HpAb-OP

283 e RNFLT parameters that achieved areas under ROC curve >=0.80 were global (0.89), supero-temporal (0.

284 on set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894.

285 here may be a larger reservoir of unmeasured ROC at the former site.

286 Using ROC analysis, the highest value of area under curve (AUC

287 Using ROC curve, we calculated the area under the curve (AUC)

288 Using ROC-curve analysis, serum HCV-RNA cut-offs for ruling in

289 Using ROC-curve analysis, serum HCV-RNA cutoffs for ruling in/

290 arker was more predictive than another using ROC curves, but multiple logistic regression suggested s

291 ectiveness to discriminate both groups using ROC curves.

292 sessed according to various thresholds using ROC analysis and time-to-event regression.

293 Utilizing ROC analysis, LVMI was found to be a stronger predictor

294 selection, achieving an external validation ROC AUC of 0.59 (95% CI 0.49-0.68), accuracy of 0.77 (95

295 POT pipeline achieved an external validation ROC AUC of 0.60 (95% CI 0.50-0.69), accuracy of 0.81 (95

296 reveal the cellular mechanism through which ROCs form the wound epidermis and ensure successful rege

297 Model accuracy was 86% with ROC-AUC of 0.96.

298 ores of 50 and 400 SNPs were identified with ROC of AUC = 0.74 and AUC = 0.94, respectively.

299 The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781)

300 ristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibited superior performance to the