ライフサイエンスコーパス: predictive

1 ver, no single parameter model in itself was predictive.

2 ter than 0.8, demonstrating that the model's predictive abilities are acceptable.

3 s study sought to prospectively evaluate the predictive ability of nGA for the conventional clinical

4 for the dichotomized score, and compared the predictive ability of the qPitt score to that of the PBS

5 Our model outperforms the predictive ability of tPSA (AUC 0.71), used in clinical

6 wo data sets enables very high (R(2) > 0.79) predictive accuracies on multiple sequence-activity pred

7 e developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%.

8 ta was more predictive than CNV data and the predictive accuracy is good for all model types.

9 on the day of CT pulmonary angiography had a predictive accuracy of 0.90 (95% CIs: 0.78-1.00) for pul

10 CIPANTS: A retrospective cohort study of the predictive accuracy of a previously validated polygenic

11 The predictive accuracy of the Ferrara unfitness assessment

12 with CNN-based deep learning, can boost the predictive accuracy.

13 Interestingly, these predictive action models were used comparably at a senso

14 recent version of this theory, the internal predictive adaptive response (iPAR) model, by assessing

15 laboratory data, the model showed excellent predictive agreement with the decrease in tumor volumes

16 While the predictive algorithm yields useful reproducible results

17 nd accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for

18 Predictive algorithms were developed based on logistic r

19 Emerging data science techniques of predictive analytics expand the quality and quantity of

20 o show that neurons from the VTA encode both predictive and incentive cues, support an important role

21 human participants (N = 21; 18 female) used predictive auditory cues to anticipate the timing of low

22 nt in SCLC, which has precluded its use as a predictive biomarker.

23 clinical outcomes should help identify novel predictive biomarkers and features as well as therapeuti

24 ortly after treatment initiation to identify predictive biomarkers differentiating responders from no

25 frequencies in early pregnancy may serve as predictive biomarkers for women who are at risk of deliv

26 t respond to ICT and the availability of the predictive biomarkers is limited.

27 candidates that may be considered potential predictive biomarkers of LGG molecular classification.

28 advanced hepatocellular carcinoma (HCC), and predictive biomarkers of response to systemic therapies

29 HNSCC biology and immunobiology to identify predictive biomarkers that will enable delivery of the m

30 g their usefulness not only as screening and predictive biomarkers, but also in capturing the pathoge

31 utations in human malignancies and represent predictive biomarkers.

32 efore, we sought to identify a more accurate predictive blood biomarker for evaluating anti-PD-1 resp

33 sentational substrates and structures in the predictive brain.

34 Therefore, predictive brains need abstract value representations.

35 mors in developing a clinically translatable predictive cancer biomarkers for cancer patients.

36 modelling competitions) can fuel advances in predictive capabilities and provide a forum for the disc

37 Here, we sought to assess the predictive capacity of the gastrointestinal microbiome a

38 rces and a consequential need to improve our predictive capacity.

39 Within a predictive coding framework sensory information could be

40 In predictive coding, experience generates predictions that

41 th prediction and its rather distant cousin, predictive coding.

42 tinct cortical layers, and rhythms implement predictive coding.

43 In this study, we present a rapid, predictive computational approach that combines a popula

44 volume of a sucrose reward associated with a predictive cue is suddenly altered, from the beginning a

45 se in the nucleus accumbens evoked by reward-predictive cues is accompanied by a rapid suppression of

46 ansfer electronics, and machine learning for predictive data analysis.

47 We show that this type of predictive distinction can give novel insight, and may d

48 Thus, the only predictive effect was between preonset activity (along w

49 The predictive effectivity of the model was validated by usi

50 Evaluating predictive epigenomic marks of smokers in peripheral blo

51 ve equations developed for healthy adults or predictive equations based on "static" variables.

52 asured by indirect calorimetry compared with predictive equations developed for healthy adults or pre

53 ng energy expenditure values calculated from predictive equations differing by +/- 10% from resting e

54 e resting energy expenditure calculated from predictive equations showed very good agreement or accur

55 Predictive equations with "dynamic" variables and respir

56 -DAA era represented an independent positive predictive factor for graft and patient survival (hazard

57 ability, and facilitate the use of TMTV as a predictive factor in DLBCL patients.

58 actors in the development of the disease and predictive factors for mortality from the years 2007 thr

59 Predictive factors for myopic growth were explored.

60 edictive models trained on only the top four predictive features (CI, asymmetry, hyperchromatism, and

61 Our most predictive features included biological function of prot

62 Foveal avascular zone size was predictive for logarithm of the minimum angle of resolut

63 ce of 2019-nCoV spike protein, we apply this predictive framework to provide novel insights into the

64 potential for root traits to be used within predictive frameworks of plant-fungal relationships.

65 The goal of predictive HTE analysis is to provide patient-centered e

66 The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, w

67 or the further development of diagnostic and predictive in vitro tests.

68 and public health research communities with predictive insights that may help study and battle this

69 Here, we present methylome association by predictive linkage to expression (MAPLE), a computationa

70 rian cancer should not be used as individual predictive markers to stratify patients who are likely t

71 th clinical parameters for the assessment of predictive markers.

72 he genetic basis of a trait should allow for predictive methods with accuracies approaching the trait

73 e the development of a more quantitative and predictive microbial community ecology.

74 We designed a probabilistic predictive model and trained it using Bayesian inference

75 A unique challenge in predictive model building for omics data has been the sm

76 Our goal was to validate a new predictive model for BPD severity that incorporates resp

77 In conclusion, a new predictive model for BPD severity that incorporates resp

78 hic variables were excluded from the optimal predictive model of cognitive decline.

79 We recently proposed a new predictive model that combines serum creatinine levels a

80 s to train a simple and easily interpretable predictive model that outperforms other existing predict

81 Validation of the predictive model was conducted in a forward-chaining set

82 A predictive model was developed using density functional

83 AVs and that the sequence-based antigenicity predictive model will be useful in understanding antigen

84 1067 of these were used to produce the predictive model.

85 n experiences for which we do not yet have a predictive model.

86 Predictive modeling explains the sorption data in consid

87 The predictive models afforded greater than 93% correctness

88 y efficiency in the chemical industry; thus, predictive models are of key importance.

89 Model components were integrated into predictive models at the cell and tissue scales to expos

90 tology alone, were also investigated and the predictive models built yielded 100% accuracy in discrim

91 his study was to develop and test multiclass predictive models for assessing the invasiveness of indi

92 from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist a

93 rmation collected from single cells to build predictive models for cell classification is demonstrate

94 Yet, predictive models for infection are missing.

95 State-of-the-art predictive models for phenotype predictions from metagen

96 challenge and affects the reproducibility of predictive models for preoperative detection of MVI in H

97 Four predictive models for progression to late AMD or atrophi

98 The incorporation of these predictive models into a decision algorithm allowed the

99 mance and interpretation of expression-based predictive models involves the aggregation of gene-level

100 The predictive models may be used to determine deployment re

101 We expect these findings to inform current predictive models of mechanical behaviour in polymer-com

102 Principal goals were: (1) to develop predictive models of NPP and GPP calibrated to source da

103 database for the United States and generate predictive models of regional plant taxonomic and phylog

104 t self-reported surveys can be used to build predictive models to identify likely COVID-19-positive i

105 Predictive models trained on only the top four predictiv

106 ANN allows for continuous improvement of the predictive models' performance, thus promising that the

107 to guide omics data collection for training predictive models, making evidence-driven decisions and

108 Using these predictive models, we provide a global-scale quantitativ

109 logical processes and for building realistic predictive models.

110 te novel difficult-to-drug targets, to apply predictive non-clinical models to select promising drug

111 Akita learns predictive nucleotide-level features of genome folding,

112 etal muscle area z scores were significantly predictive of 2-year survival (P = .04) in combined mode

113 ients with CACLD, the FLIS was independently predictive of a first hepatic decompensation (adjusted h

114 distinct rotational dynamics that were more predictive of behavior.

115 rate that basal gene expression patterns are predictive of changes in FOS correlation networks in the

116 mics data to identify biomarkers potentially predictive of complex diseases has garnered considerable

117 ine (DA) synthesis capacity, in the striatum predictive of conversion to schizophrenia.

118 od mononuclear cells (PBMCs), that is highly predictive of cytokine responses.

119 We identify molecular markers that are predictive of differentiation efficiency of individual l

120 Furthermore, the state-space locations were predictive of distinct types of errors: failures-to-stop

121 he cocaine-paired chamber, a measure that is predictive of duration in that chamber, stress increases

122 5, p = 0.003]), whereas abnormal PVR was not predictive of either (HFpEF: 0.9 [0.4-2.0, p = 0.85], HF

123 Conclusion: PSMA PET results are highly predictive of FFP at 3 y in men undergoing sRT for BCR a

124 , these early T cell activation metrics were predictive of GVHD onset 3-6 wk before phenotypic pathol

125 tifying gene expression biomarkers that were predictive of high-stress states and of future psychiatr

126 that simple C-patch preference is not fully predictive of hnRNPK localization within transcripts.

127 Our integrative approach was predictive of IA (area under the receiver operating char

128 t the predicted absolute binding affinity is predictive of immunogenicity.

129 ls in patients hospitalized for COVID-19 are predictive of in-hospital AKI and the need for dialysis.

130 mammals, and hypo- and hypermethylation were predictive of increased and decreased transcription rela

131 Translationally, these findings are predictive of initiation of the prodromal / preclinical

132 t none of the serotonin receptor genes, were predictive of interindividual differences in anxiety-lik

133 dels using transporter gene frequencies were predictive of known siderophore activity, molecular weig

134 s worse adherence behavior; values >=2.5 are predictive of late allograft rejection.

135 ates rather than retinal structural outcomes predictive of long-term vision.

136 High expression of NEK2 was predictive of low survival rates in patients who had res

137 clinical implications as VO2 peak is highly predictive of morbidity and mortality in HF.

138 se at the time of cisplatin addition was not predictive of outcome, the proliferative history of the

139 survival; however, these parameters were not predictive of overall survival (OS), which highlighted t

140 PD-L1 positivity appears to be predictive of pembrolizumab efficacy.

141 , anxiety, and sleep disturbances, which are predictive of poor treatment outcomes.

142 of progression using naive CD4+ T-cells was predictive of progression along the whole IA-continuum.

143 =-0.46, P=0.001, width: r=-0.3, P=0.047) and predictive of rapid VT termination by a single radiofreq

144 cation incorporating genomic data was highly predictive of recurrence (OR 13.20, P = 0.0197).

145 AJCC 7 Stage was not predictive of recurrence-free survival (RFS) or overall

146 Increased bacterial diversity was predictive of reduced subsequent diarrhea from age 6 to

147 esults indicated that vulva size (VS) may be predictive of reproductive performance in sows.

148 genase, and creatinine as the variables most predictive of respiratory failure in the evaluation coho

149 SLT volume by center was not predictive of secondary discard.

150 Factors that were predictive of stage 2 or 3 AKI included initial respirat

151 and if blood/graft tolerance biomarkers were predictive of successful withdrawal.

152 e at diagnosis of glaucoma (18-39 years) was predictive of surgical success.

153 the spike receptor binding domain (RBD) was predictive of survival and IgA against the viral spike p

154 , and that carcinoma-specific signatures are predictive of survival for human breast cancer patients.

155 e cases, a gene signature for HPV status was predictive of survival, even after adjustment for clinic

156 rophil activation before exposure was highly predictive of symptomatic RSV disease.

157 ep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the

158 und signs (Pseudo R-Square = 0.105) are more predictive of the post-operation histology outcome than

159 combined (Pseudo R-Square = 0.147) are more predictive of the post-operation histology outcome than

160 activation or anti-inflammatory treatment is predictive of the response in human cells.

161 he prefrontal cortex, appear to be primarily predictive of the subjective experience of fear.

162 ters included in the new classification were predictive of tooth loss after a long-term follow-up (>1

163 sessment, a positive PET-2 result was highly predictive of treatment failure.

164 tension (1.6, 1.0 to 2.5) were independently predictive of UIA, with a prevalence of 11.1% in those w

165 was performed to identify baseline features predictive of visual maintenance and improvement after 1

166 ave shown that the method proposed is highly predictive on a validation dataset consisting of 277 tar

167 mputer science and statistics that generates predictive or descriptive models by learning from traini

168 sease (PD), offering a means of developing a predictive or prognostic test.

169 seline CST and number of injections were not predictive (P >= 0.101).

170 ificant features and frequently provide high predictive performance across nine health state categori

171 ow that Tempel can significantly enhance the predictive performance compared with widely used approac

172 To improve the predictive performance of such equations, we updated the

173 We validated the predictive performance of the metabolite ratio in two su

174 The predictive performance of the model was assessed by comp

175 Consequently, predictive performance remains low, and risk quantificat

176 bronchiolitis severity, and to compare their predictive performance with a conventional scoring (refe

177 nified ensemble model to further improve the predictive performance.

178 Recent advancements in predictive pharmacology, such as the application of mult

179 compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide

180 Here we investigate the predictive potential of features generated from four dif

181 f SAB. We also identified other factors with predictive potential, although larger prospective studie

182 This model obtains significant predictive power (AUC = 0.841).

183 ts up to a factor 8; (2) rapidly improve its predictive power (or learning); (3) Consistently achieve

184 ncer biomarkers present variability in their predictive power and demonstrate limited clinical effica

185 e-level information (DeepSAV+PG) has similar predictive power as some of the best available.

186 he 10 established PTC SNPs showed a stronger predictive power compared with the clinical factors mode

187 aseline, suggesting that MDD-PRS adds unique predictive power in depression prediction.

188 The predictive power of CT-based lesion water imaging to ide

189 These results demonstrate the superior predictive power of the reduced Gompertz model, especial

190 e integrated into structural models that add predictive power or process models that add explanatory

191 siblings and find that typically most of the predictive power persists in between-sibling designs.

192 hway-level predictors did not offer superior predictive power relative to gene-level models for all c

193 This dynamical viewpoint gives predictive power that is beyond that of the biophysical

194 Diet and microbiome had the strongest predictive power, and each explained hundreds of metabol

195 , current prediction algorithms have limited predictive power, in part because they were not trained

196 sed to reduce the dimensionality and improve predictive power.

197 course of ADHD symptoms and may have modest predictive power.

198 r vision of a roadmap for the development of predictive preclinical models of human DILI.

199 This framework can be used for constructing predictive probability maps that can inform in-country d

200 ignatures if and when it has an 85% Bayesian predictive probability of success in a hypothetical phas

201 Predictive processing models of psychopathologies are no

202 These findings are in line with predictive processing theories proposing that neurons in

203 According to predictive processing theories, such reversed processing

204 view about the influence of abstractions in predictive processing, I suggest that most deliberative

205 One unexplored option to improve the predictive quality is to design consensus predictors tha

206 aling, but similar efforts to find universal predictive relationships for an organism's body nutrient

207 The best predictive risk assessment methods currently relied on a

208 We developed a predictive score system for 30-day mortality after palli

209 The developed predictive scoring system had 10 predictor variables and

210 methodology for constructing and optimizing predictive signatures has been less prominently explored

211 eloping actionable, robust, and reproducible predictive signatures of phenotypes such as clinical out

212 all seven criteria and demonstrates a highly predictive skill against our test datasets.

213 therapy and investigated previously reported predictive SNPs.

214 l and anatomical reference measurements, and predictive spectral models were constructed using a part

215 ique cohort LGG, for which SPM data was more predictive than CNV data and the predictive accuracy is

216 We develop a predictive theoretical model of the physical mechanisms

217 They also provide detailed benchmarks for predictive theories of roaming.

218 ng-based SES index (HOUSES) would serve as a predictive tool for graft failure in patients (n = 181)

219 imentally parameterized continuum model as a predictive tool for patient-specific care.

220 ts directing the encapsulation and is thus a predictive tool for understanding host-guest encapsulati

221 s to differentiate between nonpredictive and predictive tracking behaviors.

222 rmation on tropical tree life histories, our predictive understanding is no longer limited by species

223 1, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75), and negative predictive v

224 predictive value (0.74, 0.75), and negative predictive value (0.76, 0.82).

225 edictive value (one of 11), and 97% negative predictive value (132 of 136) for TOC.

226 itive predictive value (98.6%), and negative predictive value (95.6%) as compared with >=20 and >=60

227 ivity (99.3%), specificity (91.0%), positive predictive value (98.6%), and negative predictive value

228 ce interval [CI]: 81.5%, 93.6%) and negative predictive value (99.1% [1528 of 1542]; 95% CI: 98.5%, 9

229 rtex multimodal classifier had a significant predictive value (area under the curve, 0.96; 95% CI, 0.

230 Visually, the best negative predictive value (NPV) and positive predictive value (PP

231 e predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagn

232 ositive predictive value (PPV), and negative predictive value (NPV) of STs for metronidazole/ornidazo

233 ositive predictive value (PPV), and negative predictive value (NPV).

234 ), 93% specificity (132 of 142), 9% positive predictive value (one of 11), and 97% negative predictiv

235 negative predictive value (NPV) and positive predictive value (PPV) occurred when iPET was defined as

236 An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive va

237 ection rate (CDR), recall rate, and positive predictive value (PPV) were calculated for each reader,

238 Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (N

239 imated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (N

240 31/200) and 96.6% (170/176), with a positive predictive value and negative predictive value of 83.8%

241 icted more lymph nodes and improved positive predictive value and specificity when added to multipara

242 to detect cell-type markers, impacting their predictive value and suitability for integration into re

243 igands with 1.5-fold improvement in positive predictive value compared with existing tools and correc

244 The negative predictive value curves were also superimposable but rem

245 range, 25-94 years), resulting in a positive predictive value for biopsy of 43% (181 true-positive fi

246 eristic curve 0.83 [0.73-0.93]) had the best predictive value for ICU mortality with cutoff values le

247 ce per se, being heritable and having unique predictive value for long-term memory function, hippocam

248 The positive predictive value for SIRE was 25% and the negative predi

249 However, low sensitivity and low positive predictive value have led critics to argue that these to

250 fter NAC prior to surgery has prognostic and predictive value in early TNBC patients.

251 ity, positive predictive value, and negative predictive value in identifying PLWDH.

252 isk of active tuberculosis and evaluated its predictive value in independent cohorts.

253 res of tumour mutational burden did not have predictive value in patients receiving atezolizumab plus

254 tool; however, the sensitivity and positive predictive value may be lower than previously reported w

255 hology in extramotor brain regions (positive predictive value of 100%).

256 ity of 100% (95% CI 72%-100%) and a negative predictive value of 100%, as compared to a sensitivity o

257 tivity of 24% (95% CI 8%-50%) and a negative predictive value of 19% (95% CI 5%-46%) for the index te

258 sitive predictive value of 86%, and negative predictive value of 83%.

259 ith a positive predictive value and negative predictive value of 83.8% (31/37) and 50.1% (170/339), r

260 itivity of 82%, specificity of 93%, positive predictive value of 86%, and negative predictive value o

261 allowing to predict treatment outcome with a predictive value of 93.8% when combined with clinical fe

262 dence interval [CI], 15.7-84.3) and negative predictive value of 99.3% (95% CI, 97.5-99.9).

263 h a secondary finding and the lower positive predictive value of a screening result compared to an in

264 eport the patient-level CDR and the positive predictive value of AB-MR examinations after negative/be

265 g complete PVD was 53%, whereas the negative predictive value of an OCT scan showing attached vitreou

266 In the study population, the positive predictive value of an OCT scan showing complete PVD was

267 value of recall (PPV1) percentage, positive predictive value of biopsies performed percentage, sensi

268 In the same cohort, sensitivity and negative predictive value of depth of invasion, currently the bes

269 erventions on cancer outcomes as well as the predictive value of geriatric assessment in the context

270 -based testing as the standard, the negative predictive value of PCR was found to be 100%, while the

271 otype of FGR in women with AMSB and test the predictive value of placental sonographic screening to p

272 ative rate per 1000 women screened, positive predictive value of recall (PPV1) percentage, positive p

273 We investigated the predictive value of sporadic cases on outbreaks using a

274 y of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages,

275 It is critical to understand how the predictive value of the test varies with time from expos

276 and demonstrate the scientific validity and predictive value of this approach using an assortment of

277 We explored the clinical significance and predictive value of trans-ethnic variants in multiple po

278 relative performance (sensitivity, positive predictive value or PPV, and computational efficiency) o

279 As a consequence, the positive-predictive value toward ICM configuration was significan

280 ndard was 85.4% (35 of 41), and the positive predictive value was 100% (35 of 35).

281 tive value for SIRE was 25% and the negative predictive value was 100%.

282 The positive predictive value was 20.6%.

283 PCR was found to be 100%, while the positive predictive value was 79%.

284 On a per-patient basis, positive predictive value was 93.3% (95% confidence interval, 77.

285 cond ECG yielded a sensitivity (and negative predictive value) of 1.5% (66%) for AF detection, increa

286 Tumour mutational burden might have some predictive value, although blood-based measures of tumou

287 e in patients with a sensitivity, a negative predictive value, and a specificity of 71.4%, 87.5%, and

288 ificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estim

289 sitivity, specificity, positive and negative predictive value, and accuracy for LNM detection on (68)

290 ssess its sensitivity, specificity, positive predictive value, and negative predictive value in ident

291 ifier for sensitivity, specificity, positive predictive value, negative predictive value, and accurac

292 atter has been demonstrated to have a higher predictive value.

293 rve (average precision [AP; average positive predictive value]).

294 Projected predictive values mainly reflect the low frequency of tr

295 is low, a large gap exists between positive predictive values of chest CT versus those of reverse tr

296 how activating ESR1 mutations may alter the predictive values of molecular imaging agents for endocr

297 sitivity, specificity, negative and positive predictive values, and negative and positive likelihood

298 th better sensitivity, and negative/positive predictive values.

299 n techniques to select 10 of 720 potentially predictive variables from the electronic health records.

300 tracked specifically the learning of reward predictive visual features.