ライフサイエンスコーパス: predictive model

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

2 to-recovery (BTR) and constructed a recovery predictive model.

3 sine, estradiol, and griseofulvin follow the predictive model.

4 es were considered as input variables of the predictive model.

5 and uses these embedded features to build a predictive model.

6 thereby resulting in robust and reproducible predictive models.

7 est sex ratio in univariate and multivariate predictive models.

8 s of PPCPs on plants, and the development of predictive models.

9 egative effect on the accuracy of pre-miRNAs predictive models.

10 curately incorporate these interactions into predictive models.

11 s-validation was applied for calibration and predictive models.

12 ers useful biological interpretation of such predictive models.

13 e incorporation of biological knowledge into predictive models.

14 d possibly regulate subsequent adaptation of predictive models.

15 ubstantially to the performance of the final predictive models.

16 eviant stimuli in terms of their reliance on predictive models.

17 test sophisticated hypotheses and to develop predictive models.

18 and other rich datasets to create multiscale predictive models.

19 tandardized mortality ratios between the two predictive models.

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

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

22 nclear how to capture these variations using predictive models.

23 highlight a promising new surveillance tool: predictive models.

24 ype through multiplex genome engineering and predictive modeling.

25 tion from EHR data that facilitates clinical predictive modeling.

26 stic-net model to improve the performance of predictive modeling.

27 t challenges on the traditional framework of predictive modeling.

28 ing residues at the capsid interface through predictive modeling.

29 , particularly, random forests are useful in predictive modeling.

30 from taking dynamic changes into account in predictive modeling.

31 a, using a literature review, interviews and predictive modelling.

32 not mechanically coupled) reaction-diffusion predictive modeling (0.75), simple analysis of the tumor

33 assessments relying on in vitro systems and predictive models,1 vision equally applicable to ecologi

34 s work, we evaluated the performance of four predictive models, ABSOLV, COSMOtherm, KOWWIN, and SPARC

35 the generality and generalization of disease-predictive models across cohorts.

36 Collectively, our results indicate that predictive models aimed at quantifying C cycle feedbacks

37 The predictive models allowed monitoring physico-chemical ch

38 These results provide a new predictive model and correct physical basis for heteroar

39 The predictive modeling and design of biologically active RN

40 We review the state of influenza predictive modeling and discuss next steps and recommend

41 f climate drivers of ecosystem processes for predictive modeling and provide novel evidence supportin

42 These results demonstrate that predictive modelling and data-driven science can now be

43 uracy in parameterization of mechanistic and predictive models and conservation plans.

44 tree success will greatly assist in refining predictive models and forestry strategies in a changing

45 eimbursement, such a category should improve predictive models and more accurately reflect the qualit

46 Predictive models and new technologies are needed to cla

47 Predictive models and simulations that integrate physiol

48 Our work demonstrates the power of forward predictive models and the possibility of precision genet

49 on the development of gene expression-based predictive models and their implementation in clinical p

50 tment Sepsis score outperformed more complex predictive models and would be the most appropriate scor

51 is collection of experimental spectral data, predictive modeling, and informatic tools enables more e

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

53 ngful discovery of immunological principles, predictive models, and strategies for therapeutics.

54 (I = 0%).Inclusion of this polymorphism in a predictive model appeared to improve its ability to stra

55 ructure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively

56 Prematurity (CHOP-ROP) postnatal weight gain predictive model are 2 approaches for improving ROP scre

57 ictor of hydrogen bond basicity (pKBHX), and predictive models are presented for a number of hydrogen

58 This study identifies circumstances when predictive models are the most effective, and suggests t

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

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

61 evant genomic features as well as build up a predictive model based on selected features for various

62 The predictive model based on these factors was externally v

63 A predictive model based on these parameters accurately es

64 In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic propertie

65 Logistic regression was used to develop predictive models based on a first level recalibration o

66 ions shows this approach outperforms various predictive models based on genomics signatures and a wel

67 Predictive models based on identified loci also have mod

68 was highly accurate using computer-supported predictive models based on in vivo, ex vivo, and patient

69 Predictive models based on the genetic risk score strong

70 on of nonvalidated biomarker data to provide predictive model-based biomarkers for response classific

71 cedures combine heterogeneous data sets into predictive models, but they are limited to data explicit

72 Predictive models can identify patients with diabetes me

73 Source water TOC predictive models can provide water treatment utilities

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

75 nd temporally dynamic, making development of predictive models challenging.

76 ical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and ide

77 Beyond predictive model construction and validation, this paper

78 pediatric donor graft survival and superior predictive models could be created.

79 We found that predictive models could help identify subgroups of parti

80 erns, our group developed a connectome-based predictive modeling (CPM) approach.

81 Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for de

82 Predictive models demonstrate the potential of applied e

83 sults underscore the importance of employing predictive models developed in similar patient populatio

84 dditional research is needed to optimize ROP predictive model development, validation, and applicatio

85 physical performance decrement, but accurate predictive models do not exist.

86 We assessed three unique radiomics-based predictive models, each of which employed different fund

87 ive schematics to the development of robust, predictive models, empirical parameters in existing mode

88 Predictive models employing ensembles from individual co

89 population dynamics is hindered by a lack of predictive models explicitly linking habitat quality to

90 Current predictive models fail to account for the differences in

91 A predictive model for 90-day mortality including ADL and

92 gy and biotechnology, but so far there is no predictive model for accurately determining hybridizatio

93 Here we report the first predictive model for cell permeable background-free prob

94 atology consultation patients to establish a predictive model for cellulitis, which was then validate

95 A predictive model for chemotherapy toxicity was developed

96 Here we provide the first landscape predictive model for CWD based solely on soil characteri

97 e models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongs

98 A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-t

99 Finally, a predictive model for E/Z selectivity was developed using

100 estimation logistic regression to develop a predictive model for in-hospital mortality.

101 e apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill cra

102 d the data to produce a country-specific BGC predictive model for Kenya and map BGC store estimates t

103 externally validated a chemotherapy toxicity predictive model for older adults with cancer.

104 ally and in combination was used to create a predictive model for protection from CMV reactivation.

105 ergy relationship was established to offer a predictive model for reactivity of different types of C-

106 tatus significantly improved (P < 0.001) the predictive model for refractive change after flight.

107 ion of relaxors as "hopeless messes", and no predictive model for relaxor behaviour is currently avai

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

109 DFT calculations also provided a predictive model for site-selectivity and this model is

110 chanical calculations were used to develop a predictive model for substrate scope, site selectivity a

111 Therefore, we developed a predictive model for the covariation of H, D, and stem v

112 From these parameters, a risk-predictive model for the development of ICU-acquired IC

113 A powerful predictive model for the function of non-coding DNA can

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

115 ing tool for existing materials as well as a predictive model for their development.

116 tic regression analyses were used to develop predictive models for 30-day mortality, overall morbidit

117 We found predictive models for 426 dependencies (55%) by nonlinea

118 tural information and activity data to build predictive models for 72 in vivo toxicity end points usi

119 corporating biogeographical variability into predictive models for an accurate prediction of species

120 to evaluate alternative methods and develop predictive models for androgen and thyroid pathways.

121 e learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of

122 The best performing predictive models for BMI and BMI gain after one year us

123 Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA

124 e incorporation of dissolution kinetics into predictive models for environmental risks of nanomateria

125 Predictive models for firmness, chlorophyll, anthocyanin

126 Predictive models for linear sorption of solutes onto va

127 uctive to the development and improvement of predictive models for particle transport in fractured aq

128 To derive and validate predictive models for progression of acute kidney injury

129 hine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prog

130 ay be necessary to develop the most accurate predictive models for Pu and U in the environment.

131 the most effective, and suggests that using predictive models for public notification of unsafe swim

132 ve properties, and enable the development of predictive models for systematic materials design and op

133 fly review recent work on the development of predictive models for the impacts of climate change on h

134 Predictive models for the salt effect were developed bas

135 Jan 1, 2001, and Dec 31, 2008, we developed predictive models for violent offending (primary outcome

136 of population-based registers, we developed predictive models for violent reoffending for the cohort

137 Predictive models forecast a general increase in Scots p

138 Here, a predictive modeling framework was designed to evaluate t

139 Our predictive modelling framework focuses on developing a s

140 We discuss a predictive modelling framework to evaluate ecological hy

141 ort vector machines were used to construct a predictive model from RNA-Seq data for 240 neural constr

142 For designing a drug sensitivity predictive model from such a database, a natural questio

143 chine learning strategy was used to generate predictive models from these mapped databases.

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

145 Both multivariable predictive models had excellent performance (area under

146 l machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinicall

147 A best predictive model identified gray matter volume (P < .001

148 operating characteristic curve analyses and predictive models identified a cutoff CMV DNA level of 5

149 Logistic regression was used to build a predictive model in a random two-thirds of the cohort, w

150 arization (STRE) method for high-dimensional predictive modeling in complex healthcare systems.

151 ity measures, and treatment data may enhance predictive modeling in future studies.

152 size and limited generalizability of the ROP predictive models included in this review preclude their

153 Compared with a predictive model including objective clinical variables,

154 are the performance of 10 additive-dominance predictive models (including current models and proposed

155 A predictive model incorporating HLA and corneal curvature

156 c risk factors contributed positively to the predictive models incorporating traditional risk factors

157 A best predictive model indicated that a combination of multipl

158 We investigate in this study how 45 predictive models induced for data sets from 45 species,

159 The predictive model integrated the additive effects of all

160 Our predictive model is comparable in accuracy to other stat

161 Our predictive model is useful to explore the parameter spac

162 Central to the InterPro database are predictive models, known as signatures, from a range of

163 pretable or present only one of many equally predictive models, leading to a narrow understanding of

164 We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubi

165 alters the balance between the sensorium and predictive models, mediated by the pre-SMA and its conne

166 a comprehensive implementation that includes predictive modeling, multiattribute optimization, and mo

167 different types of statistical, data-driven predictive models: multiple linear regression model, bin

168 lassification approach was used to develop a predictive model of biological sex based on cortical thi

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

170 and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different tem

171 empathy as the dependent variable to test a predictive model of emotional empathy in 30 patients wit

172 Predictive model of GA vs. fibrotic scar showed sensibil

173 This work introduces the most accurate predictive model of growth cone trajectories to date, an

174 ACC TVT Registry have been used to develop a predictive model of in-hospital mortality for patients u

175 A predictive model of normal ageing was defined using mach

176 Logistic regression was used to construct a predictive model of palliative care.

177 We harness these findings into a predictive model of preliteracy, revealing that a 30-min

178 Predictive model of preserved macula vs. GA/fibrotic sca

179 A predictive model of PTDM was built on the basis of this

180 omatin and genomic features, we formulated a predictive model of RAG1 targeting to the genome.

181 ity idea in the commonly used ensemble based predictive model of Random Forests, we propose Heterogen

182 1, 2014, to March 31, 2015) and developed a predictive model of reliable improvement and reliable re

183 an opportunity to integrate the data into a predictive model of resource use by a mixed community.

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

185 We sought to develop a predictive model of SCD among US adults.

186 We present a predictive model of the immune component of IBD that inf

187 d are unnecessary for an efficient, accurate predictive model of thermal behavior of SOA.

188 The idea is to build a predictive model of tract locations, given patient and t

189 he first time incorporates imaging data in a predictive model of transcript-specific ribosome densiti

190 onse of a cells to alpha-factor to produce a predictive model of yeast polarization towards a pheromo

191 Our work highlights predictive modeling of circadian clock machinery and exp

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

193 the development of detailed SCP networks for predictive modeling of emergent whole cell functions.

194 ypothesis that social cognition involves the predictive modeling of others' attentional states.

195 hese results provide a basis for broad-based predictive modeling of plant gene expression in the fiel

196 to develop parameters that can be applied in predictive modeling of the fate of surfactants in the en

197 e presented fingerprinting approach provided predictive modeling of the gastrointestinal metabolome i

198 Predictive modeling of these processes remains an open c

199 this issue will enable consistent and robust predictive modeling of this phenomenon for different app

200 To analyze predictors and develop predictive models of anatomic outcome in neovascular age

201 n arm to reach for an object, the brain uses predictive models of both limb dynamics and target prope

202 (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from c

203 able logistic regression determined the best predictive models of cognitive impairment.

204 stationarity, show promise for use in future predictive models of demand.

205 early stages of FIP pathogenesis and develop predictive models of disease onset.

206 Despite this effort, general, predictive models of enhancer function are currently lac

207 uence information alone and also outperforms predictive models of episomal assays.

208 Such transitions are rarely captured by predictive models of fire behavior and, thus, complicate

209 To develop predictive models of future coastal change we need funda

210 lymorphisms are sufficient to build accurate predictive models of gene expression.

211 ration of diverse IBD data sets to construct predictive models of IBD.

212 The different predictive models of liver retrievability using liver bi

213 sequence data on coagulation factor VIII and predictive models of molecular evolution, we engineer pr

214 levant pH values, there is a need for robust predictive models of organic cation sorption coefficient

215 can serve as the foundation for successful, predictive models of particle rearrangement dynamics in

216 Predictive models of POP content versus storage time wer

217 Existing predictive models of risk of disease progression in chro

218 d environment, with potential for developing predictive models of skin phenotypes tailored to individ

219 from MC or MD simulations and XRC data into predictive models of TF binding and compared their perfo

220 ts are vital ingredients in physically-based predictive models of the earthquake source.

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

222 original simplex approach helping to develop predictive models of the proportions of co-occurring cul

223 nomic profiles, we need to identify improved predictive models of the relationship between genome and

224 Statistical predictive models of these outcomes were obtained using

225 in the design of these systems, we developed predictive models of virus attenuation that account for

226 Current predictive models often assume that the labels of the la

227 training and validation sets and develop 360 predictive models on six clinical endpoints of varying p

228 re and complexity and their consequences for predictive model outputs.

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

230 composition (were the input variables of the predictive model), prediction models were learned from d

231 atient-level factors contributed the most to predictive models (R 7.0% [c-statistic 0.67]); predictio

232 Creating a predictive model reduced the number of gene transcripts

233 This predictive model should be considered when discussing th

234 Regression analysis of predictive model simulation results reveals the relative

235 ard regression analysis was used to set up a predictive model simultaneously exploring the effects of

236 We refined the features to predictive models (support vector machine, elastic net)

237 develop severe ROP using telemedicine and a predictive model synergistically.

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

239 These were used to define a blood predictive model that accurately identified rejection in

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

241 ard stepwise logistic regression, we built a predictive model that discriminated between E and NE are

242 9 bZIP coiled-coil interactions to develop a predictive model that exploits knowledge of structurally

243 ave been used to predict TF binding sites, a predictive model that jointly considers CS and DS has no

244 erogeneous samples will perform similar to a predictive model that takes into consideration the heter

245 des an integrative framework of omics-driven predictive modelling that is broadly applicable to guide

246 e or clinical outcome, allowing for building predictive models that are not only robust to normalizat

247 sis and batch effect adjustment for use with predictive models that are validated and fixed on histor

248 idden in existing fluxomic data will lead to predictive models that can significantly accelerate flux

249 turn, led to the development of quantitative predictive models that describe catalyst performance.

250 y in recent decades allow the development of predictive models that inform the design of molecules wi

251 We built predictive models that operate on arbitrary temporal pat

252 s of quantitative neuroscience is to develop predictive models that relate the sensory or motor strea

253 Predictive models that we propose are based on easily ha

254 -learning algorithms, building generalizable predictive models that will be useful in the criminal ju

255 We developed a predictive model, the Incidence Patterns Model (IPM), re

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

257 In this work, we develop an open-source predictive model to accurately simulate the most common

258 ry, the PCA-LDA analysis was used to build a predictive model to identify and quantify automatically

259 of 2007 to 2008 using a novel, nonparametric predictive model to identify those residents who are at

260 We built a Partial Least Squares (PLS) predictive model to quantify the relationship between th

261 n of tailored psychophysical experiments and predictive modeling to address this question with regard

262 Linking predictive modeling to private well use information nati

263 multiple participants and the value of using predictive models to constrain analysis.

264 We also used the predictive models to convert a naturally occurring ChR i

265 in silico and these were capable of building predictive models to infer the metabolic adaptations of

266 Generalizable predictive models (trained by out-of-sample fit and base

267 sitivity of A. tridentata, we developed four predictive models, two based on empirically derived spat

268 t that current emission inventories based on predictive modeling underestimate levels of atmospheric

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

270 in protein stability as evidence to train a predictive model using a representative set of protein-l

271 A predictive model using five common variables (age, sex,

272 A simple summation risk-predictive model using the 10 independently significant

273 patient data prevent widespread practice of predictive modeling using EHRs.

274 after delivery: a pharmacokinetic model and predictive models using deletion/substitution/addition o

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

276 data needed to train effective disease onset predictive models using longitudinal electronic health r

277 xplored the feasibility that radiomics-based predictive models using pre- and post-treatment computed

278 characterized cancer cell lines and trained predictive models using standard methods like elastic ne

279 g trigonometric functions of those angles in predictive models, using "harmonic analysis." We applied

280 Our new predictive model was capable of achieving a correlation

281 Finally, the performance of the developed predictive model was evaluated in HILIC enriched glycope

282 The predictive model was evaluated using two standard hold-o

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

284 peri-implant disease or implant loss, and a predictive model was tested.

285 A predictive model was trained with the measured luminesce

286 Predictive modeling was used to identify early risk fact

287 For the predictive model, we use multivariate regression trees f

288 Linear regression and a best predictive model were developed to determine the associa

289 Predictive models were developed by using more than one

290 Three predictive models were developed using covariates of (1)

291 Predictive models were employed to efficiently mine the

292 Predictive models were trained and validated on a cohort

293 rs of BCR activation, and present a minimal, predictive model where clustering receptors leads to the

294 sequences to be searched against InterPro's predictive models, which are provided by its member data

295 velopment of an online calculator using this predictive model will allow us to identify patients who

296 Field trials and predictive models will be required to assess the product

297 Using an integrated approach of predictive modeling with determination of the native Msp

298 characteristic curve (AUCs) achieved by the predictive models with identified non-SMGs as predictors

299 n errors, favoring the selective updating of predictive models with larger prediction errors.

300 cular failure were used to derive an initial predictive model, with a second (day 2) model including