ライフサイエンスコーパス: predictive

1 their movements to be largely reactive, not predictive.

2 tion in these systems is critical to improve predictive abilities for future climate scenarios.

3 d across datasets argues for its competitive predictive ability (AUC of 0.80+/-0.18, AUPR of 0.84+/-0

4 weeks' postmenstrual age indicated that the predictive ability for serious respiratory morbidity inc

5 These data demonstrate the predictive ability of baseline CIWA-Ar score, age, and s

6 To examine the predictive ability of coronary artery calcium (CAC) scor

7 eveloped, but none to date have assessed the predictive ability of lung function in a population-base

8 ntly support predictions based on the higher predictive ability of progeny environmental cues.

9 inated the red from the white rice bran with predictive ability of the model showed a satisfactory cl

10 The predictive ability of the models was confirmed by an ext

11 asuring frailty in surgical populations with predictive ability on par with other frailty tools.

12 pal commercial waterways, adding significant predictive ability to near- and long-term forecasts of f

13 inations of questionnaire items improved the predictive ability with respect to severe disease beyond

14 Third, predictive accuracy and interpretability must be recogni

15 is produces optimal diversity and collective predictive accuracy.

16 of the underlying biology, while maintaining predictive accuracy.

17 al history, and to develop models to improve predictive accuracy.

18 y index significantly improved its mortality predictive accuracy.

19 ry of novel markers to improve diagnostic or predictive accuracy.

20 ofoundly reduced spatially selective, choice-predictive activity in single neurons and delayed choice

21 aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide

22 d ratios (HRs) and C statistics to determine predictive and discriminatory values.

23 ches to operationalize molecular testing for predictive and prognostic molecular biomarkers involve s

24 vocal non-learners may have the capacity for predictive and tempo-flexible synchronization to a beat,

25 pathological case-control series and show a predictive AUC of 84%.

26 However predictive biases co-exist with an independent source of

27 resents a paradigm shift from descriptive to predictive biology.

28 A high basal level of micronuclei as a predictive biomarker for AsiDNA treatment was validated

29 imaging can serve as a quantitatively useful predictive biomarker for efficacious responses to cancer

30 ine whether (18)F-clofarabine may serve as a predictive biomarker for responses to dCK-dependent prod

31 facilitating the clinical use of c-myc as a predictive biomarker for this treatment.Significance: Du

32 te myeloid leukemia and propose its use as a predictive biomarker.

33 dicating that tag diversity may be used as a predictive biomarker.

34 Predictive biomarkers and mechanistic explorations will

35 Predictive biomarkers and novel clinical trial end point

36 RIS are incompletely defined and no reliable predictive biomarkers exist.

37 ich, like in human patients, could represent predictive biomarkers for risk evaluation.

38 Here we characterized potential predictive biomarkers for treatment with AsiDNA, a novel

39 tient population, and highlight the need for predictive biomarkers in the treatment of esophageal can

40 e expression signatures are commonly used as predictive biomarkers, but do not capture structural fea

41 hemokines predicted C-IRIS and are potential predictive biomarkers.

42 Excellent predictive capability of FT-EoS was observed with an ove

43 The accuracy of a predictive classifier, encompassing cortical and subcort

44 orized contribution of NMDAR hypofunction to predictive coding deficits in schizophrenia.

45 Disruption of NMDARs causes dysfunction in predictive coding during vocalization in a manner simila

46 ffects of the NMDAR antagonist, ketamine, on predictive coding during vocalization in healthy volunte

47 ed the response patterns hypothesized by the predictive coding model, whereas posterior insula encode

48 Theories like predictive coding propose that lower-order brain areas c

49 Predictive coding suggests that the brain infers the cau

50 etical models of the neural instantiation of predictive coding.

51 rion at various postmenstrual ages were less predictive compared with those using the criterion of ox

52 Here, we constructed and validated a predictive computational model of the cardiac mechano-si

53 We tested whether a predictive context that was embedded in a rapid visual s

54 l of maternal-diet induced obesity to define predictive correlations between maternal factors and off

55 ew, primarily inhibitory responses to reward-predictive cues across learning.

56 Predictive cysteine cross-linking in E. coli membranes a

57 significant momentum by the virtue of their predictive design, controllable porosity, and long-range

58 ontrolling system behaviour, and facilitates predictive-design of motility-based pattern formation.

59 n barrier pathology as a clinically relevant predictive, diagnostic and pharmaco!dynamics biomarker f

60 as observed ( P = .61), whereas a borderline predictive effect ( P = .04) was observed with a deleter

61 ' CVD and CKD risk groups had multiplicative predictive effects, with no evidence of an interaction (

62 ) but did not show significant prognostic or predictive effects.

63 e heterogeneity in ARDS, both prognostic and predictive enrichment strategies are needed that target

64 Moreover, NPM1m PB-MRD may be used as a predictive factor for ASCT indication.

65 tor B was the most important single negative predictive factor for indication for step-down treatment

66 cause of renal failure, represented the only predictive factor for MGUS development.

67 ODH is an independent predictive factor for the development of POAG in patient

68 ues above the third quartile may have been a predictive factor.

69 eated in which point values were assigned to predictive factors and final risk score is correlated wi

70 present a new methodology for investigating predictive factors associated with development of vision

71 Predictive factors for erosive tooth wear were assessed

72 ients with ocular hypertension (OHT) and the predictive factors for ODH are very similar to those for

73 istance mechanisms with the need to identify predictive factors of therapy response.

74 ng on the definition, incidence, mechanisms, predictive factors, and management of structural degener

75 her, these data identify MSC morphology as a predictive feature of MSC immunosuppressive function.

76 include suggestions for visualizing the most predictive features (i.e., brain connections).

77 ether the HDL cholesterol efflux capacity is predictive for cardiovascular risk.

78 pression levels of any of these 16 genes are predictive for post-stroke blood brain barrier (BBB) dis

79 with age, but responses at birth were poorly predictive for those at ages 1 and 3 years.

80 lation are well understood, a systems-level, predictive framework synthesizing those details is curre

81 The availability of predictive imaging biomarkers of inflammation and neurol

82 CI, 1.57-6.65; P = .001) were independently predictive in multivariable analysis.

83 channels in vitro and using these data in a predictive in silico model of the adult human ventricula

84 he DHCR7 polymorphism may be a pre-treatment predictive marker for response to PEG-IFN-based therapy

85 type-specific NAb titers may be a meaningful predictive marker that allows patient stratification by

86 longase and desaturase activities for use as predictive markers for T2DM remission after Roux-en-Y ga

87 Predictive markers of cancer aggressiveness were identif

88 These factors were correlated in a predictive mathematical model designed to guide prognosi

89 between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeos

90 dence that the DPPS is dynamically shaped by predictive mechanisms run by the motor system and based

91 incorporating advances in biotechnology and predictive methodologies into alternative testing strate

92 Using a predictive model (AUC = 0.77), physician preference larg

93 ified according to IA status and developed a predictive model based on genetic risk, established clin

94 disclosed by our lab, we sought to develop a predictive model for site selectivity and extend this ar

95 ly, we propose a highly accurate exome-based predictive model for the MSI phenotype.

96 nts, recruited in Toulouse, was used to test predictive model generalization ("test" sample).

97 Our predictive model is comparable in accuracy to other stat

98 d the inferred pathway activities to build a predictive model of cisplatin response.

99 Predictive model of GA vs. fibrotic scar showed sensibil

100 1, 2014, to March 31, 2015) and developed a predictive model of reliable improvement and reliable re

101 To generate a multidimensional predictive model of risk factors for iatrogenic withdraw

102 Regression analysis of predictive model simulation results reveals the relative

103 Our observations are consistent with a predictive model that assumes metacommunity dynamics and

104 Applying the species-based predictive model to a base map of species distribution i

105 n each of the five training sets, we built a predictive model using a least absolute shrinkage and se

106 In this work, an oxidation predictive model was proposed, following a methodical co

107 ors for developing IA and were combined in a predictive model.

108 rstanding these parameters for use in future predictive modeling of eDNA transport.

109 a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad a

110 ype through multiplex genome engineering and predictive modeling.

111 ingle cell transcriptomics data and to build predictive models of the gene regulatory networks that d

112 ing feature weights were used to establish 3 predictive models per binning configuration: one model b

113 ic data were employed to create multivariate predictive models using learning machine techniques.

114 s framework may help advance theory, improve predictive models, and inform new approaches to effectiv

115 e learning methods are used to construct the predictive models, capturing the future risks of GDM in

116 s of final macular status, and developed two predictive models.

117 es data and thus for the design of adequate, predictive models.

118 when an immediate reward was given for every predictive movement.

119 SuPAR is independently predictive of adverse outcomes, and its addition to a 3-

120 2.09; 95% CI, 1.30-3.37; P = 0.003) were all predictive of better visual outcome after treatment with

121 and age, sarcopenia index was independently predictive of both hospital (p = 0.001) and 90-day morta

122 Results SNP88 was predictive of breast cancer risk overall (interquartile

123 in/kexin type 9 (PCSK9) has been shown to be predictive of cardiovascular events (CVEs) in patients w

124 verity, and persistent organ dysfunction are predictive of chronic critical illness.

125 ), and TRIB1 copy number and expression were predictive of clinical outcome.

126 ework reveals chromatin features that may be predictive of clinical response to epigenetic therapy.

127 metabolic activity early after infection is predictive of disease outcome.

128 Ki67, a marker of proliferative capacity, is predictive of expression of viral proteins, and downregu

129 tally derived networks, while remaining more predictive of expression than motif-based networks.

130 g even in the absence of hunger), which were predictive of having a high body mass index (BMI) and be

131 nging from 0 to 5 or higher that were highly predictive of HCC recurrence (C statistic, 0.77).

132 Beta power is predictive of healthy and abnormal behaviors, including

133 l: 1.06, 1.79) was found to be independently predictive of hepatic decompensation.

134 doxine vs placebo and to identify biomarkers predictive of HFS.

135 PET, negative scans, indicating a CMR, were predictive of improved 1-y survival, duration of respons

136 .71, and 0.70) HF risk scores were similarly predictive of in-hospital and 30-, 90-, and 180-day post

137 s-specific, formed stable attractors and was predictive of memory content.

138 The KT ratio remained significantly predictive of mortality even after adjustment for the ad

139 :(18)F-FDG PET/CT after 1 treatment cycle is predictive of outcome to first-line chemotherapy with be

140 ," tumor size greater than 2 cm was the most predictive of outcome.

141 among a list of 22 variables those that were predictive of overall survival (OS).

142 e response genes (CCL22, IL2RB, and IRF4) is predictive of patient survival.

143 hree main clinical risk factors and are more predictive of poor outcomes than the rate of development

144 cal role in promoting thyroid cancer that is predictive of poorer patient outcome.

145 r in ER positive breast cancer patients, and predictive of preclinical sensitivity to this drug combi

146 designed by machine learning to be maximally predictive of rearrangements.

147 Cirrhosis was predictive of reduced SVR (0.51 [95% confidence interval

148 prefrontal cortex circuit in a model highly predictive of relapse highlights the importance of socia

149 nal connectivity during reward processing is predictive of response to a psychotherapy modality that

150 ay compensate for transmission delays and be predictive of rhythmic changes.

151 , multiunit, or fMRI responses are much less predictive of seizure activity.

152 frequency, and whether blood eosinophils are predictive of sputum eosinophils.

153 Activity in this network was further predictive of stimulus value updating indicating a compa

154 e how levels of cortical FDG metabolism were predictive of subsequent cognitive decline rated with th

155 the time of neutrophil recovery post-HCT is predictive of subsequent development of severe acute GVH

156 apse in attention or mind wandering as being predictive of subsequent reductions in sensory processin

157 NA amplifications and transcript fusions and predictive of telomerase activity.

158 ux (+1 point), and depression (+1 point) was predictive of the "persistently frequent" trajectory (ar

159 ynamic time warping and seven other features predictive of the human consensus.

160 eral SMA were negatively correlated with and predictive of transfer.

161 PSMA PET is independently predictive of treatment response to SRT and stratifies m

162 ed that the decrease in MMP-9 levels was not predictive of treatment response.

163 in the promoter region, which was perfectly predictive of vernalization response in 216 wild and dom

164 DL1 and HLA-B subtype combinations that were predictive of weak inhibition or noninhibition were asso

165 should be added to other variables that are predictive of weight gain to inform the design of obesit

166 ents who had a positive repeated culture was predictive of worse clinical outcome than those who achi

167 Cast data from in vitro screening assays and predictive pathway models, high-throughput toxicokinetic

168 ng of the method demonstrates an increase in predictive performance compared with state-of-the-art me

169 It has an improved predictive performance due to the training on updated, h

170 ized selection method and then evaluated the predictive performance of each model in the correspondin

171 To assess the predictive performance of our virtual reaction extractio

172 determine the localization accuracy and its predictive performance, the individual and combined test

173 ctivity in single neurons and delayed choice-predictive population dynamics.

174 odents; however, this feature showed limited predictive power due to high inter- and intra-animal var

175 To improve the predictive power of first-principles calculations, there

176 erall, this investigation highlights the (i) predictive power of nonadiabatic quantum treatments of p

177 However, the predictive power of nucleic acid-ligand scoring function

178 EP as a biophysical feature can improve the predictive power of quantitative binding specificity mod

179 beling followed by MS markedly increased the predictive power of the integrative modeling strategy en

180 Moreover, the poor predictive power of the KDPI for adult donors appears to

181 ng peptides by in silico algorithms, but the predictive power of this approach is unclear.

182 ds is key to their prospective applications, predictive power over these structural factors remains e

183 The predictive power was similar to the TC model (IQ-OR, 1.4

184 count the tissue hierarchy leads to improved predictive power.

185 c variants in schizophrenia with significant predictive power.

186 llenge to TB drug development is the lack of predictive pre-clinical tools.

187 It involves both predictive processes linked to action control, and retro

188 populations, and is consistent with current 'predictive processing' theories of psychosis.

189 izophrenia has been associated with impaired predictive processing, but the underlying mechanisms tha

190 assification of breast cancer and the use of predictive/prognostic molecular signatures for guiding t

191 e results were used to develop computational predictive quantitative pharmacology models.

192 We show that the answer takes the form of a predictive representation.

193 ophil cationic protein as an independent and predictive risk factor for thrombotic events in humans.

194 9 G>T and BTRC rs61873997 G>A) that showed a predictive role in CM-specific survival, with an effect-

195 ous work evaluated individual prognostic and predictive roles of TP53, KRAS, and EGFR in non-small-ce

196 We found that monkeys could generate predictive saccades synchronized to periodic visual stim

197 Validation of this novel predictive score is needed to confirm clinical utility.

198 activation, the latter being consistent with predictive semantic computation of alternatives to the n

199 Thus, a predictive signal about the upcoming movement is widely

200 e purpose of this study was to elucidate the predictive significance of MPV in CRC.

201 les that are activated selectively by reward-predictive stimuli.

202 tion of patients into appropriate prognostic/predictive subgroups.

203 standard logistic regression approaches were predictive, they were minimally interpretable.

204 for the results obtained but also creates a predictive tool toward a quantitative approach.

205 computational calculations can be used as a predictive tool.

206 pportunities are discussed for extending the predictive translational value of mouse research, with a

207 were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting

208 Each phenotype also possesses clinical and predictive utility.

209 stress=0.55, r with sleep quality=0.39) and predictive validity (area under curve=0.88).

210 pathophysiological resemblance with BD) and predictive validity (responsiveness to treatments used i

211 ethod for the ICU-7 scores demonstrated good predictive validity with higher odds (odds ratio = 1.47;

212 receiver operating characteristic curve for predictive validity.

213 icity (91%), and positive (93%) and negative predictive value (100%) for ISH positivity.

214 ositive predictive value (PPV), and negative predictive value (NPV) of RDTs were 51.7%, 94.1%, 67.3%,

215 rate, 12.6% (95% CI: 12.5%, 12.7%); positive predictive value (PPV) of a biopsy recommendation (PPV2)

216 PCR, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (N

217 range, 91%-99%; PPV range, 0%-3.6%; negative predictive value [NPV] range, >/=99%).

218 equired neurosurgical intervention (negative predictive value [NPV], 100.0% [95% CI: 99.9%-100.0%]).

219 The negative predictive value and sensitivity of troponin concentrati

220 approach had a significantly higher positive predictive value compared to minimum inhibitory concentr

221 The model has revealed a strong predictive value for baseline cholinesterase and bilirub

222 Regardless of the classification, predictive value for development of periodontitis in you

223 teria also improved specificity and positive predictive value for HCC (R1, two fewer false-positive f

224 ients, total LGE but not LGE border zone had predictive value for ICD therapy.

225 losis infection (LTBI) are limited by a poor predictive value for identifying people at the highest r

226 e and irreversible myocardial injury and its predictive value for left ventricular remodeling.

227 ATRIA, ORBIT and HEMORR2HAGES improved their predictive value for major bleeding leading to improved

228 ation between the duration of monitoring and predictive value for mortality (R = 0.78; p < 0.001).

229 Positive predictive value for MR imaging recalls was 9.3% (95% CI

230 lume (area under the curve 0.83) showed good predictive value for new-onset AF.

231 ity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma

232 ity, positive predictive value, and negative predictive value for PLC injuries were 55% (11 of 20), 9

233 oplastic neurological disorders and has high predictive value for SCLC.

234 parameters exceeded thresholds, the negative predictive value for survival above 1 y was 79%.

235 combined diaschisis measures had a positive predictive value for survival below 1 y of 100%.

236 rmore, theta oscillatory activity may have a predictive value for the clinical benefit after chronic

237 justed alpha = .0125), with highest positive predictive value in phase 1 (64.0%) and highest negative

238 alue in phase 1 (64.0%) and highest negative predictive value in phase 2 (90.2%).

239 pecificity of 93.1% (364 of 391), a positive predictive value of 74.8% (80 of 107), and a negative pr

240 r was below the threshold, it had a positive predictive value of 75%, and when both parameters exceed

241 e value of 74.8% (80 of 107), and a negative predictive value of 78.1% (364 of 466).

242 511) and B (n = 127) demonstrated a positive predictive value of 78.4% for "clinical AERD," which ros

243 minate Quantiferon-CMV result had a positive predictive value of 83% and a negative predictive value

244 ve value of 95.5% (21 of 22), and a negative predictive value of 83.3% (10 of 12).

245 ity, positive predictive value, and negative predictive value of 85.3%, 93.9%, 27.4%, and 99.6%, resp

246 whereas a cutoff of 641 IU/L had a negative predictive value of 88%.

247 sensitivity, 84% specificity, and a positive predictive value of 90% for >/=70% stenosis.

248 or M65, a cutoff of 2000 IU/L had a positive predictive value of 91%, whereas a cutoff of 641 IU/L ha

249 were successfully extubated, for a positive predictive value of 92%.

250 11), accuracy of 91.2% (31 of 34), positive predictive value of 95.5% (21 of 22), and a negative pre

251 ity, positive predictive value, and negative predictive value of 96.0% (95% confidence interval [CI],

252 itive predictive value of 83% and a negative predictive value of 98% for identifying participants at

253 th CRC with 87.0% sensitivity and a negative predictive value of 99.4%.

254 e of 97.2% (95% CI 85.0-100), and a negative predictive value of 99.6% (97.9-100).

255 and meta-analysis of studies evaluating the predictive value of acute MRI lesion patterns for discri

256 cluded cancer detection rate (CDR), positive predictive value of biopsy recommendation (PPV2), sensit

257 (at least 1 of the 3 features), the positive predictive value of confocal microscopy was 87.5% and th

258 The negative predictive value of CTC for adenomas >/=6 mm was 90.7% (

259 e considered the reference standard, and the predictive value of diameter and volume changes was anal

260 However, little is known about the predictive value of frequently measured ST2 levels in pa

261 The negative predictive value of programmatic early success was <20%.

262 Given the high sensitivity and negative predictive value of results obtained, BacterioScan 216Dx

263 rate of anaphylaxis, fatal outcomes, modest predictive value of ST, resource requirements for ST, an

264 e responses to particular stimuli beyond the predictive value of stimulus intensity or self-reports o

265 Herein, sST2 improved the predictive value of the API (AUC=0.70, 95% CI 0.56-0.84)

266 Prospective trials are needed to assess the predictive value of the circulating biomarkers.

267 ssion was undertaken to evaluate the optimal predictive value of the RD-OGI Score.

268 The negative predictive value of the staged algorithm was 99.5% (1530

269 The positive predictive value of tissue transglutaminase type 2 (tTG)

270 95% CI 0.56-0.84), but had also significant predictive value on its own (AUC=0.65, 95% CI 0.52-0.79)

271 Critically, these metagenes also have predictive value regarding tumor grade and patient outco

272 The positive predictive value was 11% and the negative predictive val

273 nfocal microscopy was 87.5% and the negative predictive value was 58.5%.

274 94.5) and for adenomas >/=10 mm the negative predictive value was 98.6% (95% CI, 97.0-100).

275 ve predictive value was 11% and the negative predictive value was more than 99%.

276 The clinical variables of greatest predictive value were coma (31% had seizures; odds ratio

277 n = 95), specificity(82% vs. 62%), positive predictive value(66% vs. 50%) and area under curve (0.81

278 patients who lived at least a year (positive predictive value, 45.2%).

279 s low risk for the primary outcome (negative predictive value, 98.4%; 95% confidence interval [CI], 9

280 (P<0.001) with similar performance (negative predictive value, 99.7%; 95% CI, 99.4%-99.9%; sensitivit

281 Sensitivity, specificity, positive predictive value, and negative predictive value for NLP

282 Sensitivity, specificity, positive predictive value, and negative predictive value for PLC

283 nstrating sensitivity, specificity, positive predictive value, and negative predictive value of 85.3%

284 es with a sensitivity, specificity, positive predictive value, and negative predictive value of 96.0%

285 Considering its positive predictive value, it might allow to make a considerable

286 in part to a lack of robust biomarkers with predictive value, some optimism has come from the identi

287 nical outcome scores to determine a possible predictive value.

288 ostic sensitivity, specificity, and negative predictive values (70% to 100%) but low positive predict

289 ictive values (70% to 100%) but low positive predictive values (below 50%).

290 The negative predictive values (NPVs) for rayon swabs and ESwab speci

291 cancer consistently resulted in low positive predictive values (PPVs) and false-positive rates, with

292 in image contrast, sensitivity, or positive predictive values between the 2 (68)Ga-OPS202 peptide do

293 gorithm yielded an average of 95.8% positive predictive values for both cases and control subjects.

294 vity, specificity, and positive and negative predictive values for malignant tumors of the conjunctiv

295 was reflected in poor positive and negative predictive values for treatment failure.

296 sk cirrhosis generates positive and negative predictive values of 80% and 86%, respectively.

297 itivity, specificity, positive, and negative predictive values of 84%, 80%, 64%, and 92%, respectivel

298 The sensitivity, specificity, and predictive values of NSBH, and FeNO, as well as sputum e

299 However, the low positive predictive values of symptoms elicited in primary mental

300 .S. were underrepresented with arsenic data, predictive variables available in national data sets wer