ライフサイエンスコーパス: predictive model

1 1067 of these were used to produce the predictive model.

2 scribed novel techniques and established RNA predictive model.

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

4 ng SBRT and could be combined in an accurate predictive model.

5 riable analysis and were used to develop the predictive model.

6 icted probability statistic generated by the predictive model.

7 bstantially improve the MC1R red hair colour predictive model.

8 tive CRM were then assessed by formulating a predictive model.

9 ng SBRT and could be combined in an accurate predictive model.

10 a target outcome of interest and building a predictive model.

11 logical processes and for building realistic predictive models.

12 rks with transfer learning to train accurate predictive models.

13 further analyzed using CHAID to produce two predictive models.

14 area under the curve of existing, validated predictive models.

15 e to quickly recover when holding dissimilar predictive models.

16 types for follow-up studies or in developing predictive models.

17 larly deep learning) in developing TGx-based predictive models.

18 7686 patients were included in learning our predictive models.

19 fied bio-interaction information to generate predictive models.

20 l power necessary for building more accurate predictive models.

21 s to permit the construction of mathematical predictive models.

22 l MRI dataset and multivariate pattern-based predictive models.

23 est original hypotheses using descriptive or predictive modeling.

24 ed may inform therapeutic development and/or predictive modelling.

25 ling local and global cellular constrains in predictive modelling.

26 aracterize within explanatory (p < 0.05) and predictive models (10% increase in accuracy).

27 and the application of a more sophisticated predictive model, a well-parametrized support vector mac

28 The best performing predictive models (accuracies of ~80%) have been include

29 The predictive models afforded greater than 93% correctness

30 ck cognate 71 kDa protein into a multimarker predictive model along with previously identified risk f

31 error against DFT-computed properties, such predictive models also inherit the DFT-computation discr

32 A predictive model analysis using unstimulated analytes di

33 rameters was applied to the development of a predictive model and to gain insight into features drivi

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

35 ce and recognition represents a challenge to predictive modeling and computational techniques being d

36 e to any research area that may benefit from predictive modeling and feature identification using reg

37 genetics, unsupervised cluster analysis, and predictive modeling and found that a previously unreport

38 These predictive models and calculator may help to inform clin

39 ned with the advancement of the field toward predictive models and fundamental understanding of these

40 Nevertheless, the lack of predictive models and studies based on the forgetting pr

41 outbreaks with the ultimate goal of training predictive models and to identify the most important fac

42 Each analyte class was included in predictive modeling, and area under the receiver operato

43 , we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significa

44 s employ diverse molecular features to build predictive models, and while some algorithms are cancer-

45 a from 265 participants, we used a validated predictive model approach that allows the full assessmen

46 The predictive modelling approach adopted in this paper prov

47 s for a diverse set of TCGA cancer types and predictive modeling approaches.

48 Many predictive models are available but their applicability

49 Considering that predictive models are based on biological and statistica

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

51 hould be applicable in all disciplines where predictive models are sought and informative yet approxi

52 Predictive models are useful tools for aqueous adsorptio

53 e Hybrid model with individual dose-response predictive models at desired time points.

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

55 With the predictive model based on estimated body cell mass and a

56 rived and externally validated a simple risk predictive model based on recipient characteristics at H

57 ed for determining rice botanic origin using predictive modeling based on support vector machine (SVM

58 vides a promising learning tool for building predictive models based on multi-source genomic data.

59 nformation such as transcriptome alignments, predictive models based on sequence profiles, and compar

60 , we recommend prospective validation of any predictive model before implementing it into clinical pr

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

62 Instead, predictive models built on the true pathway mappings led

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

64 a modelling dataset; and (iii) constructing predictive models by learning inherent correlation betwe

65 wrote this perspective to share how we think predictive models can be integrated into medicinal chemi

66 Predictive models can enhance the salience of unanticipa

67 Broadly applicable, these predictive models can use standard medical images to cla

68 ve a clinical risk score using multivariable predictive modelling, considering factors at hospital ad

69 h-risk individuals at an earlier stage using predictive modeling could lead to improved preventive an

70 hat the sustained attention connectome-based predictive model (CPM), a validated model of sustained a

71 cocaine abstinence by using connectome-based predictive modeling (CPM), a recently developed machine

72 Connectome-based predictive modeling (CPM)-a recently developed, whole-br

73 ng scores, and HAM-D scores created a robust predictive model (DeltaF(3, 22) = 16.75, p < 0.001, Delt

74 to aid with ecotoxicological assessments and predictive model development, the tool's applicability e

75 ivalent Manifold (GEM) framework to generate predictive models, each posing a different experimentall

76 utilizes artificial intelligence to generate predictive models efficiently and more effectively than

77 owever, it remains challenging to build good predictive models especially when the sample size is lim

78 Predictive modeling explains the sorption data in consid

79 g the polygenic score derived from it into a predictive model for bleaching in the wild.

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

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

82 Using our predictive model for chromatin compaction, we develop a

83 platform was used to generate a multimarker predictive model for DHFA.

84 is typically results in the fitting of a new predictive model for each specific type of setup, which

85 ivariable regression was used to calculate a predictive model for early-onset FGR (birthweight centil

86 ional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation.

87 ter ICD implantation provides an alternative predictive model for individual risk of death or ICD sho

88 e lack of an objective and easily applicable predictive model for its detection at the time of T1DM d

89 A robust and simple predictive model for ligand effect on reactivity is ther

90 ying etiologies of liver disease to create a predictive model for liver fibrosis based upon a microbi

91 A predictive model for methane activation catalysis follow

92 ion in this field by describing an objective predictive model for non-remission, an easy tool for mon

93 key steps are necessary: the generation of a predictive model for non-remission, the adoption of a us

94 Purpose To develop and validate a predictive model for postembolization syndrome (PES) fol

95 same approach was also applied to develop a predictive model for reactivity of different alkyl halid

96 housands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumili

97 ne scaffold has for PI3Kdelta and provides a predictive model for the activity against the PI3K isofo

98 We also present a simple predictive model for the design of configurationally sta

99 this study is to assess the feasibility of a predictive model for tumor motion estimation in three-di

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

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

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

103 ng Massachusetts birth record data, to build predictive models for cord serum polychlorinated bipheny

104 luding the World Health Organization rely on predictive models for developing strategies and setting

105 hat medial frontal cortex maintains separate predictive models for different sensory domains, but eng

106 The lack of accurate predictive models for fragment ion intensities impairs t

107 terize the heterogeneity of LD and developed predictive models for identifying medications that are a

108 a-coefficients generating 8 surgery-specific predictive models for IH occurrence, all of which demons

109 Yet, predictive models for infection are missing.

110 unsolved fundamental questions that preclude predictive models for microbial transport and spreading

111 We describe predictive models for mortality in pediatric acute respi

112 DA, kNN, and SVM methods were used to create predictive models for olive oil classification.

113 big data era and can be extended to develop predictive models for other complex toxicity end points.

114 ical distributions were employed to generate predictive models for over 4000 OTU that leverage easy-t

115 a high-order tensor which is used to develop predictive models for overall survival.

116 ne gene-environment interactions and compare predictive models for PD.

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

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

119 Four predictive models for progression to late AMD or atrophi

120 features relevant to reactivity and to build predictive models for reactive trajectories.

121 Predictive models for stillbirth were developed using mu

122 romoter and use machine learning to generate predictive models for the downstream core promoter regio

123 teresting example that illustrates why older predictive models for the sensitivity values of energeti

124 t plausible risk indicators and provided two predictive models for use in a particular university set

125 The most predictive models for weight loss included features of d

126 We formalize these considerations into a predictive modeling framework and demonstrate its use fo

127 mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard

128 nmental change, partly due to the absence of predictive modeling frameworks that can mechanistically

129 Yet, when building predictive models from brain data, it is often unclear h

130 a "one size fits all" approach to developing predictive models from REIMS data is not appropriate.

131 Included in SMARTS is a predictive modeling functionality that can, with high ac

132 Predictive models have been used with caries risk tools,

133 Predictive models have succeeded in distinguishing betwe

134 d a systems approach to data integration and predictive modeling, highlight the applicability of thes

135 The best predictive models, however, obtained from ANN analysis w

136 Predictive modeling identifies which lesions will progre

137 However, predictive models identifying who is prone to develop AU

138 ll, this demonstrates that holding different predictive models impacts synchronization in musicians p

139 aim to evaluate the use of DTI and DTT as a predictive model in the field of epilepsy, specifically

140 ard neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-cataly

141 Predictive modeling in the training set using ElasticNet

142 d the performance of five BRCA mutation risk predictive models in a Chinese cohort of 647 women, who

143 of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the

144 The best predictive models in the training set were obtained by c

145 The best predictive models in the training set were obtained by c

146 In addition, the PCa predictive model including age, fPSA and complexed PSA,

147 quantified with reasonable accuracy using a predictive model incorporating patient age, sex, tumor h

148 Predictive models incorporating 3-month changes in DRIL

149 Two predictive models incorporating ADC(mean)-to-ADC(NAWM) r

150 The new highly predictive models indicate common physiologic pathways t

151 Predictive modeling indicates that Lrch4 LRRs conform to

152 the way for microbiome-based development of predictive models, individualized treatment plans, and n

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

154 Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal

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

156 The predictive model is trained with data from reference sta

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

158 Our simple, predictive model may open the door to the engineering us

159 Ultimately, such predictive models may aid oncologists with making critic

160 The predictive models may be used to determine deployment re

161 We developed a predictive model of 30-day readmission among hospitalize

162 This is the first predictive model of carbon emissions flux from any propo

163 the efficacy of this approach for building a predictive model of catalytic behavior for a homologous

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

165 A diagnostic and predictive model of early-stage keratoconus was calculat

166 Finally, we derive a predictive model of future proliferation behavior in C.

167 actions by tag extension (SPRITE) to build a predictive model of gene expression.

168 (obviating screening) and (2) to simplify a predictive model of HF outcomes by only using cognitive

169 In this study, we developed a predictive model of in vivo stent based drug release and

170 le logistic regression was used to develop a predictive model of LN metastases which was internally v

171 and their tempo, which were extracted from a predictive model of musical structure based on Markov ch

172 Together, these findings establish a predictive model of neocortical GABAergic interneuron my

173 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme envi

174 A predictive model of renal damage was constructed using t

175 udy is an important step toward developing a predictive model of the structural variables that dictat

176 We developed a predictive model of the voxel-wise response and further

177 stochastic rules, that embodies an implicit predictive model of the world.

178 In this work, we expand a predictive model of trace element behavior at coal-fired

179 Predictive modeling of 291 microbial and metabolite valu

180 model is an important starting point for the predictive modeling of cell fate decisions that include

181 cular and multi-scale modeling in general in predictive modeling of chemical modulation of biological

182 ations provides a way forward to improve the predictive modeling of small molecule bioactivities and

183 The predictive modeling of the Raman spectroscopic data is p

184 Predictive modelling of CH(4) was conducted by partial l

185 ew opportunities for preventative action and predictive modelling of vector borne disease risks in re

186 Predictive models of base flow nitrate concentrations in

187 Currently, a significant barrier to building predictive models of cellular self-assembly processes is

188 Here, we develop predictive models of current pest distributions and test

189 Including apathy in predictive models of dementia improved model fit.

190 Predictive models of DNA chromatin profile (i.e. epigene

191 ntibiotic combinations lead to more accurate predictive models of gene expression with 44% less data.

192 ovide an unprecedented opportunity to create predictive models of gene expression with far reaching a

193 s (TWAS) gene-level tests based on multi-SNP predictive models of gene expression-for identifying cau

194 e the big data necessary to develop complex, predictive models of gene regulation.

195 Emerging multi-scale predictive models of HE response to loads account for th

196 rse bacteria to generate datasets that allow predictive models of how phage-mediated selection will s

197 ared representations of tasks augmented with predictive models of human capabilities and actions.

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

199 criminant analyses were performed to develop predictive models of mild MPP and severe MPP on these ch

200 The two cohorts were then combined to derive predictive models of NASH and disease activity by nonalc

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

202 Current predictive models of organic cation sorption assume that

203 Our findings indicate that predictive models of outcomes in cancer may be developed

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

205 r kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence.

206 tope-specific antibody binding, we developed predictive models of SU.

207 data to estimate heritable risk and develop predictive models of this heritability.

208 ancer-related genes to train race-stratified predictive models of tumor expression from germline geno

209 r the manipulation of participants' internal predictive models of upcoming events.

210 m the brain's ability to make inferences, or predictive models, of sensory information.

211 the minimal training set required to build a predictive model on a per-patient basis.

212 improved performance and supports validating predictive models on local data.

213 ine learning methods to make highly accurate predictive models over a broad range of selectivity spac

214 Predictive models perform well in the Central Equatorial

215 howed feature selection methods to influence predictive model performance, describing and evaluating

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

217 ants demonstrated greater precision in their predictive model, reflected in a larger prediction error

218 However, predictive modeling requires incorporation of realistic

219 Predictive modeling results suggested 90% are toxicants.

220 The challenge is to link predictive modeling results with the experimental data c

221 Predictive modeling revealed that maximum grip aperture,

222 Analysis of predictive models revealed robust contributions from pat

223 remains elusive, invoking an urgent need for predictive models seamlessly integrating metabolism with

224 In a predictive modeling setting, if sufficient details of th

225 ine the spreading of degeneration, we used a predictive modelling strategy that tests whether baselin

226 We then applied our predictive modelling strategy to an exploratory whole-br

227 The predictive model suggested that the 3-day moving average

228 The current predictive modeling techniques applied to Density Functi

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

230 We propose a predictive model that considers, for every molecule, the

231 mechanism allows the formulation of a simple predictive model that explains experimentally observed t

232 Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV

233 ent odorants and used these data to refine a predictive model that links odorant structure to odorant

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

235 regions (UTRs) with deep learning to build a predictive model that relates human 5' UTR sequence to t

236 Predictive models that accurately emulate complex scient

237 e microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical p

238 The predictive models that describe the fate and transport o

239 , these approaches have been used to develop predictive models that identify skin diseases, ranging f

240 ays have shown improvement over conventional predictive models that include one or multiple datasets.

241 y enabling the development of more realistic predictive models that incorporate diverse variables and

242 nd between hosts, as well as efforts towards predictive models that leverage microbiome genetics.

243 We developed a predictive model to classify a separate, family-based co

244 The use of an automated predictive model to identify high-risk patients for whom

245 We use this predictive model to quantify the global spatial patterns

246 , tower-based CO(2) flux measurements, and a predictive model to simulate seasonal canopy color dynam

247 We also applied predictive modeling to estimate toxicity.

248 ibility (liDNase-Seq) profiling, followed by predictive modeling to identify putative transcriptional

249 we propose to leverage digital pathology and predictive modeling to select the most discriminative fe

250 , and a feature selection that optimizes the predictive models to a specific disorder predictor.

251 We used multiple linear regression and predictive models to assess the correlations between ant

252 g noncoding RNAs (lncRNAs) and corresponding predictive models to diagnose these patients.

253 better sensitivity and specificity, building predictive models to distinguish metals from non-metal t

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

255 severity of the disease is highly variable, predictive models to stratify patients according to thei

256 le-modality predictors, and a multi-modality predictive model, to identify the independent and combin

257 en, subject-level approach, connectome-based predictive modeling, to resting-state functional magneti

258 Predictive models trained on only the top four predictiv

259 We show that predictive models trained using property data are the mo

260 cal trends that run contrary to experimental predictive models under future climate scenarios.

261 We compared a predictive model using time-varying features to a model

262 Advances in the field of predictive modeling using artificial intelligence and ma

263 and enhancement but is often unsuitable for predictive modeling using features without spatial corre

264 Multivariate predictive modeling using the Elastic Net (EN) algorithm

265 We developed predictive models using combined data from 2 cohorts (20

266 Predictive models using multivariable smoothing explaine

267 rrent research is aiming to develop accurate predictive models using patient history, ultrasound and

268 earning algorithms were compared to generate predictive models using REIMS data to classify beef qual

269 ient samples were obtained and used to build predictive models using the least absolute shrinkage and

270 We identified several strong predictive models, using size and shape features the hig

271 Thus, we aimed to develop a novel predictive model-using machine learning methods on elect

272 -way interactions were used to build a joint predictive model via stepwise regression, in which the p

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

274 A month 3 predictive model was constructed from clinical and prote

275 The predictive model was developed by using conditional infe

276 After imputing missing features, a predictive model was developed on a randomly sampled set

277 A predictive model was developed using density functional

278 gulator analysis, clustering techniques, and predictive modeling, we show that baseline samples are i

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

280 Histology-specific predictive models were also constructed with an AUC that

281 uNGAL performance and predictive models were assessed using the area under the

282 ere implemented, and 10-fold cross-validated predictive models were built for each.

283 Predictive models were built using a common data model t

284 After a feature selection step, two predictive models were built with ordinal regression: Mo

285 Results of predictive models were consistent with empirical potenci

286 Cox regression analysis were performed, and predictive models were constructed.

287 Partial least squares predictive models were developed and incorporated into t

288 Predictive models were developed and validated in genera

289 Predictive models were developed for conjugation efficie

290 Predictive models were developed for eligible adult and

291 In this retrospective study, predictive models were developed to identify undiagnosed

292 to elucidate the genetic architecture, while predictive models were tested to prove that VOCs can be

293 The lack of accurate predictive models, which are essential for the design of

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

295 stigate the amount of data necessary to make predictive models with different modeling methods.

296 ysiological, and behavioral information into predictive models with epidemiological applications.

297 oimaging components and examined univariable predictive models with single-modality predictors, and a

298 eveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses f

299 work will help researchers construct better predictive models, with different formalisms, that will

300 A predictive model would help direct subsequent surgical t