ライフサイエンスコーパス: model based on

1 FFMI and FMI models based on 1079 children, aged 2-21 y, were created

2 liter using a random forest machine-learning model based on 11 geospatial environmental parameters an

3 In the multivariate time-varying model, based on 666 patients with available cytochrome P

4 Another model based on a 15-gene panel was developed to differen

5 Here we present a biomimetic cancer model based on a collagen matrix synthesized through a b

6 understanding, we generated a systems-based model based on a dataset of 32 LQT3 patients, which then

7 used Bayesian networks to train a prediction model based on a dataset of 70,694 publications from PLo

8 We propose a model based on a double-negative feedback loop, vertical

9 Moreover, an extended model based on a factorization of the sparse-variable GL

10 In this study, an analytical model based on a fractional bulk density (FBD) concept w

11 spectacle correction was described best by a model based on a human development index, with adjustmen

12 A linear forecasting model based on a logic matrix decision tree enabled an a

13 primary human fallopian tube epithelial cell model based on a method previously established for cultu

14 Here we describe a new mtDNA mutator model based on a mitochondrially-targeted cytidine deami

15 ere we present a new three-dimensional wound model based on a reconstructed human epidermis (RHE).

16 We present a model based on a ring-shaped lattice potential, which al

17 nd clinically verified a risk stratification model based on a second TE biopsy confirmation and segme

18 ion current rectification (ICR), a continuum model based on a set of Poisson-Nernst-Planck and Stokes

19 We propose a cell cycle model based on a single trigger and sequential releases

20 e profiles, we develop a simple mathematical model based on a stochastic control process and use it t

21 We consider a simplified model based on a stochastic growth process driven by a c

22 A finite element crack band model, based on a recently developed anisotropic spheroc

23 Conclusion: Mixed models based on a historical cohort of patients with com

24 biological experiments using cell and animal models based on a hypothesis built from the epidemiologi

25 We found that simple models based on a log-spaced spectrogram with approximat

26 ons of dissolved oxygen (DO) and mechanistic models based on a representation of biophysical processe

27 the tsunami, with state-of the-art numerical models, based on a combined landslide-source and bathyme

28 computational efforts and demonstrates that models based on accurate coronary physiology can improve

29 Dice scores greater than 0.8, on par with a model based on all complete and incomplete data.

30 We set up a mechanistic computational model based on allosteric principles to simulate calmodu

31 We offer a model based on ammonium extraction and surface ion-pair

32 A deep learning model based on an ensemble of encoder-decoder architectu

33 0.05), vs 0.63 ( +/- 0.01)), p < 0.05, while models based on analogous aggregate imaging features did

34 Here we use computational modeling based on analysis of fifteen primary breast tum

35 nte Carlo based simulation and deep learning models based on artificial neural networks can prove hig

36 of work (i.e., person-knowledge) and unknown models based on attractiveness only.

37 spatial analysis and artificial intelligence modeling based on Bayesian network among 194 World Healt

38 Simulation models based on big data from EHRs can test clinic chang

39 ilation by constructing a new synthetic cell model based on bio-derived coacervate vesicles with high

40 Here, we use a general model based on biochemical kinetics to quantify the comb

41 A mass balance model based on bovine serum albumin-water (D(BSA/w)) and

42 this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-se

43 ere we propose an averaged and deterministic model, based on cell population dynamics, replicative se

44 Cancer models based on cells derived from human embryonic stem

45 ing late AMD development was similar for the models based on CFP alone (model 1; 0.80), OCT alone (mo

46 Conclusion A risk model based on chest radiographic and laboratory finding

47 g electron microscopy (SEM), and biophysical modeling based on classic Hertz theory to elucidate how

48 ormed with equal or superior accuracy to the model based on clinical comorbidities.

49 Random Forest models based on clinical data and sequencing results wer

50 del of growth arrest, yet were easily fit by models based on collective cell behavior, for example in

51 Two prediction models based on colorimetric analysis allow estimation o

52 However, a classification model based on combined bacterial and viral microbiota p

53 his universality by proposing a mathematical model based on communicative and cultural memory, which

54 We developed and validated a model based on comorbidity burden, Model for End-Stage L

55 In this study, we propose a model based on complex networks of weakly connected dyna

56 re, MyDEP, which implements several particle models based on concentric shells with adjustable dielec

57 tiple modeling techniques and identified top models based on consensus.

58 in the analysis incorporates realistic field models based on considerable new field data and models f

59 elopment), to revise a large-scale signaling model based on context-specific data and identify main r

60 opose a general mechanochemical polarization model based on coupling between a stochastic model for t

61 lament were consistent with expectation from models based on crystallography, x-ray diffraction, and

62 ht recent examples of large-scale ecological models based on data integration and outline the concept

63 re already incorporated into epidemiological models based on data of transmission dynamics, particula

64 A model based on DD-SIMCA was also developed and applied t

65 We present a joint model based on deep learning that is designed to inpaint

66 We construct a model based on delay differential equations and paramete

67 A mechanical model based on density functional theory calculations an

68 Stress models based on density functional theory calculations s

69 However, this model (and modified models based on descriptors incorporating atropselective

70 ree to within 2% AGB with a species-specific model based on detailed manual crown mapping of 3D tree

71 an existing machine learning (ML) prediction modeling based on DFT computations and is comparable to

72 We introduce a lattice model based on dipolar interactions plus a competing, fr

73 e within a general framework, we show that a model based on Divergent Allele Advantage (DAA) provides

74 , a scoring function for ranking alternative models based on diverse types of data, and a sampling me

75 The prediction model based on DL achieved an area under the receiver op

76 broken by mutations designed using homology models based on Dpr and DIP structures.

77 weeks in 2016 was used to train three linear models based on drinking water production, electricity c

78 Finally, we developed PLSR models based on dry-film FTIR spectroscopy for the predi

79 The discriminant models based on E-nose dataset enable a 100% correct cla

80 y) was estimated using a land-use regression model based on each subject's residential postal code.

81 e rise of angiosperms, rejecting alternative models based on either climate change or time alone.

82 imed to test this hypothesis by constructing models based on either genes alone, or based on sample s

83 ter align with species distributions than do models based on either temperature or oxygen alone.

84 ffect of PRP, we also present a mathematical model based on electrical circuits.

85 Models based on electrochemical potential have been pres

86 A theoretical model based on electrostatic and surface energies shows

87 Models based on element predictors showed accuracies ran

88 I show that a mathematical model based on environmental stochasticity, the stochast

89 Empirical habitat suitability models based on environmental conditions can augment sur

90 Adding CRP to prediction models based on established risk factors improved model

91 With the predictive model based on estimated body cell mass and a "predictio

92 Here, we present an alternative model based on evidence that tinnitus is: (1) rare in pe

93 Here we provide a quantitative model, based on experiments, for the non-brittle, fluid

94 rved frequency of hits by a statistical null model based on exposed surface areas of atom types in th

95 The models based on feature extraction exhibited higher pred

96 We developed a model based on fecal viral diversity and clinical data t

97 Bayesian modeling based on fingerprinting elements suggested that

98 Using Wannier tight-binding models based on first-principle calculations, we link th

99 new target sample to be imputed, outperform models based on fixed gene relationships.

100 We construct a mesoscale semianalytical model based on force-dependent bond rupture and show tha

101 ed nuclear import kinetics of 30 large cargo models based on four capsid-like particles in the size r

102 We then integrate across these models based on four key elements-level of analysis, con

103 Partial least squares regression (PLSR) models based on full spectra showed higher precision (R(

104 Our argument uses a simple analytical model based on Gaussian optics, numerical propagation ca

105 ding to IA status and developed a predictive model based on genetic risk, established clinical risk f

106 Using a model based on geographical preferential attachment, we

107 We review problems with evaluating bifactor models based on global model fit statistics.

108 Fourth, we found that models based on global network architecture and nodal ef

109 significantly higher than a random model or models based on gray matter volumes, degree, strength, a

110 ion of associative memory in a computational model based on Hebbian cell assemblies in the presence o

111 Our model based on Hertz theory showed that the interactions

112 In addition, the model based on high-energy triplet reactivity found that

113 ype 2 cell (Th2)-mediated allergic CRS mouse model, based on house dust mite (HDM) and Staphylococcus

114 Using a theoretical model based on hydrodynamic singularities, we capture qu

115 Hence, a modified diffusion model based on hydrodynamic theory is proposed to separa

116 e we show how a fitness-maximising space use model, based on IFD, gives rise to resource and consumer

117 QSAR models based on IL-1beta were able to predict the inflam

118 has led to the development of geostatistical models based on in situ observations of dissolved oxygen

119 This is the first assessment of Omega in models based on in situ observations.

120 We present a model, based on increasingly refined geometric parameter

121 nd must be inferred by constructing internal models based on indirect evidence.

122 The latter was estimated using a model based on infectious dose and the sensitivity of nu

123 We then present a mathematical model, based on inferred functional interactions between

124 The Chick-Watson inactivation kinetic model, based on integral CT (ICT) dose, well fitted the

125 We also developed a multivariate model based on integration-site distributions and found

126 The models based on interval-PLS efficiently (NSE >= 0.90) p

127 Our computational model based on intracellular energy homeostasis successf

128 It is concluded that a model based on intracortical inhibition can account well

129 Random-effects models, based on inverse variance weights, were conducte

130 ultivoxel pattern analysis and computational modeling based on inverted encoding model simulations.

131 The developed database models based on IR fingerprint spectroscopy with chemome

132 hich can be explained using a simple circuit model based on junction capacitances, confirmed by densi

133 actin is surprisingly weak, and we propose a model based on kinetic trapping to explain how affinity

134 A theoretical model based on kinetic Wulff construction theory and den

135 oying more geographically detailed diffusion models based on known spatial features of interpersonal

136 that fusing experimental cues with in silico models, based on known biochemistry, can contribute with

137 We then analysed kinase specificity models, based on known target sites, observing that spec

138 et, we also compared our models with another model based on KT in the United States.

139 Predicted fluxes from previously published models based on lakes, reservoirs, and agricultural wate

140 astoma on clinical scans is feasible using a model based on large, heterogeneous, and partially incom

141 from individual seeds were used to build ML models based on LDA algorithm.

142 A robust numerical model based on LES method was developed and successfully

143 Classification and regression tree models based on level of alanine aminotransferase and ab

144 ree of ionicity around oxygen, which extends models based on linear Li-O-Li configurations.

145 A competitive model based on logistic regression with LASSO achieved a

146 f the nano-sized probe neglected in previous models based on low-frequency assumptions.

147 ructural studies on bacterial outer surfaces models, based on LPS monolayers at air-water interfaces,

148 on-automated ML approach produced an optimal model based on LR using 16 out of the 23 features from t

149 (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors

150 used to validate a tumor control probability model based on M (FMISO) The prognostic potential with r

151 Remarkably, our model based on macroscopic variables succeeds despite co

152 Among them, the prediction model based on mathematical epidemiology (SIR) is the mo

153 PM (10%) in a syngeneic 4T1-luc breast tumor model based on measurements of tumor volume, 4T1-luc bre

154 Stereochemical models, based on mechanistic and DFT studies, demonstrat

155 e experimental results through a theoretical model based on Mie theory.

156 This approach allows build classification models based on MIR data achieving 85% and 89% of accura

157 ithin the framework of a minimal theoretical model based on "mitogen competition." We propose that th

158 ansplantation was determined by joint-effect model (based on Model for End-Stage Liver Disease [MELD]

159 Therefore, we devise a unified data model based on molecular similarity networks for represe

160 ensional infrared spectroscopy, and spectral modeling based on molecular dynamics simulations.

161 ly explained in terms of static lock-and-key models based on molecular complementarity.

162 ated using a Bayesian structural time-series model based on mortality trends in similar states.

163 es of chronic colitis were confirmed in this model, based on MRC and histopathology.

164 Modeling based on mRNA half-lives suggests that most deg

165 mising learning tool for building predictive models based on multi-source genomic data.

166 We present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with

167 We present a novel hybrid model, based on multiparametric intensities, which combi

168 Africa to train and validate a random forest model based on multispectral and environmental variables

169 Next, we tested classification models based on nasal methylation for atopy or atopic as

170 Purpose To develop a risk model based on negative mammograms that identifies women

171 We developed a flexible community assembly model based on neutral theory to ask: How do dispersal,

172 A signal transduction model based on nonlinear direct cell killing accounted f

173 mostly consistent with surface partitioning models based on octanol-air partition coefficients (K(oa

174 ined EEG and SNP features model outperformed models based on only EEG features or only SNP features f

175 A simplified PLSR model based on optimal wavelengths showed a good perform

176 We established an in vitro mouse PCa model based on organoid technology that takes into accou

177 son of teeth and implants via general linear models based on orthogonal polynomials showed similar re

178 A random walk model based on our observed data estimates a positive ne

179 Additionally, by using a prediction model based on our previous cohort we accurately assigne

180 Mathematical modeling based on our data provides estimation of the cl

181 Models based on overnutrition with adipose restriction/i

182 driven by HbP2, and find that the prevailing model, based on pairwise cooperative binding of Bcd to H

183 We propose a model based on paracrine signalling to account for the s

184 Two recent cryo-EM structures, and a third model based on partial high- and low-resolution structur

185 Prediction models based on pathway scores are more robust to degrad

186 asymptotic analysis, we derive a simplified model based on physiological data and compare our result

187 By employing a toy model based on point charges on a surface, and comparing

188 ctive concentrations follow simple geometric models based on polymer physics, offering an indirect me

189 A model based on polymerizing actin filaments pushing agai

190 validation random groups, we found that the models based on pooling samples from various geographic

191 icky to measure directly, and homogenization models based on porosity are often used as a proxy.

192 d and refine an improved sigma(N)-holoenzyme model based on previously published 3.8-A resolution X-r

193 Using a network model based on primate large-scale white matter neuroana

194 retations open new prospects for formulating models based on proper effective intermolecular potentia

195 ining may boost the performance of a smaller model based on public and site-specific data.Supplementa

196 Onsager proposed a statistical hydrodynamic model based on quantized vortices.

197 an entropic-electrostatic-interfacial (EEI) model, based on quasi-equilibrium free-energy minimizati

198 accurately identified using machine learning models based on readily available clinical data and may

199 However, our models based on recent data make more robust predictions

200 ethodical approaches and provides structural models based on recent findings about the plasticity of

201 xternally validated a simple risk predictive model based on recipient characteristics at HT that has

202 Flexible parametric models based on relative survival were used to estimate

203 two-component biochemical reaction-diffusion model based on relaxation oscillators and couple this to

204 Here we used an in vitro model based on remineralization of mouse dental tissues

205 Head and neck cancer (HNC) risk prediction models based on risk factor profiles have not yet been d

206 independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0

207 In predicting MD, deep learning models based on RNFL en face images achieved an R(2) of

208 this study was to develop a machine-learning model based on routine, quantitative, and easily measure

209 Risk prediction models based on routinely collected health data perform

210 We use a theoretical model based on scanning electron microscope (SEM) images

211 such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to fe

212 supervised analysis capable of constructing models based on simple and intelligible rules.

213 her lack explicit statistical models, or use models based on simplistic assumptions.

214 We evaluated pre-HD stratification models based on single visit resting-state functional MR

215 These results show that data-driven modelling based on spatial datasets and model-data fusin

216 introduce a new, to our knowledge, numerical model based on spectral methods for analysis of fluoresc

217 PLS-DA models based on spectroscopic data were able to classify

218 According to a simple model based on spin statistics, the injected charges for

219 We generate a 4-tier risk model based on SPINT1 concentrations, where the highest

220 two alterations is suggested by theoretical models based on striatal dopamine's topographic modulati

221 However, H(c2)(T) is well-described by a model based on strong coupling superconductivity with a

222 features, which were used in classification model based on Support-Vector Machine.

223 rmining rice botanic origin using predictive modeling based on support vector machine (SVM).

224 an observationally calibrated and validated model, based on temperature and season, which reduced th

225 terning is the beam-film model, a mechanical model based on the accumulation and redistribution of cr

226 We developed a new model based on the architecture of the semantic segmenta

227 A homology model based on the Bombyx mori EH crystal structure was

228 simple and rather widely applicable Coulomb model based on the characteristics of the molecular orbi

229 Here, we report an osteogenic tumor mouse model based on the conditional knockout of liver kinase

230 Using a statistical model based on the Cox method of modulated renewal proce

231 have been here corroborated by a theoretical model based on the diffusion equation.

232 of grid patterns in rodents and a grid-cell model based on the eigenvectors of the successor represe

233 stand the trends in activation, we propose a model based on the electronic promotion energy required

234 A model based on the Elliott-Yafet spin-flip scatterings i

235 I, Robinson et al. use a logistic regression model based on the fecal metabolome that is able to dist

236 t of this, a three-dimensional thermokinetic model based on the finite element method was developed t

237 ifts show the expected oscillations, and our model based on the gapless Dirac fermion with impurity s

238 wire, which is explained using an analytical model based on the general kinetic momentum theorem.

239 We introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows

240 ture patterning, we developed a mathematical model based on the Gierer-Meinhardt system of equations.

241 re additionally supported by the theoretical model based on the Gross-Pitaevskii equation.

242 Here, using a mouse model based on the human SLE susceptibility locus TNFAIP

243 We developed a computational BC model based on the inner-ear fluid-inertia mechanism, an

244 adaptive responses can emerge from a simple model based on the integration of fixed filters operatin

245 We show that HE2RNA, a model based on the integration of multiple data modes, c

246 ee-dimensional pore-scale reactive transport model based on the lattice Boltzmann method has been dev

247 Marcus presented an insightful thermodynamic model based on the Marcus reaction theory coupled with a

248 ckbone of C2469, as suggested by a molecular model based on the MM-GBSA approach.

249 escribe the development of a humanized mouse model based on the NOD-scid IL2rg(null) (NSG) mouse to s

250 A mechanics model based on the occurrence of adhesion and roughness

251 biotic regimens, we developed a mathematical model based on the overarching assumption that phenotypi

252 udy, we established a nongenetic FECD animal model based on the physiologic outcome of CE susceptibil

253 We aimed to develop a prediction model based on the PIRO concept (Predisposition, Injury,

254 estigate this question using a computational model based on the Potts model coupled to the dynamics o

255 the partial least squares regression (PLSR) model based on the raw data.

256 as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism.

257 Any genome-scale model based on the Systems Biology Markup Language can b

258 sional free-surface slender cylindrical flow model based on the three-dimensional axisymmetric Navier

259 problem, we first design a sinogram filling model based on the use of Residual-in-Residual Dense Blo

260 A hierarchical cluster model based on the volatilome profiles was then created.

261 ingle computational integration-to-threshold model, based on the assumption that the second guess is

262 Indeed, the clutch model, based on the exclusive dependence of cell mechani

263 ters that affect liquid motion and propose a model, based on the experimentally and numerically obser

264 heid enlargement and final dimensions can be modeled based on the direct effect of water potential on

265 n static brine and brine under agitation was modeled based on the generalization of Fick's second dif

266 Molecular modeling based on the binding site map revealed two dist

267 s incompressible steady flow with turbulence modelling based on the system Reynolds number at the ori

268 Computational models based on the accumulation of evidence to a decisi

269 These finds are consistent with models based on the arrival of multiple waves of H. sapi

270 Here we constructed computational models based on the canonical feedforward neuronal circu

271 issues by constructing the first plant clock models based on the S-System formalism originally develo

272 Finally, interpreting the NN-LFER models based on the Shapley values suggested that not co

273 onstructing the underlying scientific mental models based on the text being read.

274 machining on the joint strength, image based models, based on the observed microstructure, have been

275 lated from EHR timestamps and the simulation models based on them with observed timings.

276 A model based on these 3 variables identified patients who

277 onstructed a three-dimensional computational model based on these laws, with all parameters based on

278 Our circuit-level model, based on these four principles, explains behavior

279 Modeling based on these structures suggested different p

280 features, from various types, trains single models based on these features and finally integrates th

281 a hippocampal-neocortical neurocomputational model based on this assumption successfully simulates an

282 k Additionally, by developing a mathematical model based on this biphasic lag time distribution, we q

283 The microbial SOC model based on this concept reproduces long-term data (r

284 Here we tested whether a dynamical systems model based on this hypothesis reproduces observed patte

285 early T cell development, and a mathematical model based on this network recapitulates multistep tran

286 A theoretical model based on this proposal describes quantitatively th

287 Cross-validated prediction models based on this signature similarly classified T2D.

288 By combining models based on three-dimensional (3D) optical coherence

289 Various models based on tissue explants, isolated cardiomyocytes

290 re used to train feed-forward neural network models based on tissue volume or graph-theory measures f

291 While models based on true pathway scores are not more robust

292 a model using time-invariant features and to models based on two prior published approaches.

293 Epidemiologic risk factor-based models based on two simple risk factors (prior antibioti

294 e expression information than the equivalent models based on ungrouped genes.

295 model performed better when compared with a model based on US patients.

296 dels were a generative analysis-by-synthesis model (based on variational autoencoders) for MNIST and

297 in young children using data from multicause models based on vital registration and verbal autopsy.

298 agittal kinematics using multiple regression models based on walking speed, gender, age and BMI as pr

299 aining set of known anti-CRISPRs, we built a model based on XGBoost ranking.

300 eveloped an analytical stomatal optimization model based on xylem hydraulics (SOX) to predict plant r