ライフサイエンスコーパス: model-based

1 es of reinforcement learning: model-free and model-based.

2 We previously developed MethylMix, a model-based algorithmic approach to identify epigenetica

3 ms that cache outcome values in actions and "model-based" algorithms that map actions to outcomes.

4 Yet both behavioral and model-based analyses suggest domain specific differences

5 ision-making and highlight how comprehensive model-based analyses using sequential sampling models ca

6 Using a model-based analysis approach to account for individual

7 Rigorous model-based analysis can help inform state-level energy

8 was estimated non-invasively (nICP) using a model-based analysis of cerebral blood velocity and arte

9 Using a model-based analysis of functional neuroimaging data, we

10 ainst publicly available ChIP-Seq data using Model-based Analysis of Regulation of Gene Expression to

11 This model-based analysis revealed that accumbal Gln-to-Glu r

12 agnetoencephalography neuroimaging data with model-based analysis.

13 It comprises parallel systems, model based and model free, that respectively generate f

14 three independent approaches: deep-sequence, model-based and data-driven.

15 tatus relates to an imbalance in reliance on model-based and model-free control, and that it may do s

16 o joint kinematics, the proposed data-driven model-based approach also estimated several biomechanica

17 Our model-based approach can be applied to other diseases wi

18 tients compared to in vitro and to suggest a model-based approach for accounting for the potential di

19 regression and poststratification (MRP) is a model-based approach for estimating a population paramet

20 nal efforts but can be accounted for using a model-based approach for translating in vitro to human d

21 s at risk of HIV acquisition compared with a model-based approach or reliance on known risk groups an

22 We present SAME-clustering, a mixture model-based approach that takes clustering solutions fro

23 In a recent work we have developed a new model-based approach to carry out subclonal deconvolutio

24 We propose a model-based approach to combine study estimates that may

25 otentially large set of covariates through a model-based approach to standardization may provide a us

26 In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the cl

27 t an order of magnitude faster than a recent model-based approach.

28 h important and timely; however, traditional model-based approaches are stymied by uncertainty surrou

29 t PLATO has improved performance compared to model-based approaches for two key steps in TRMN predict

30 dynamics using a combination of feature- and model-based approaches on time series of 2D organoid con

31 chemotherapy was quantified using different model-based approaches.

32 s kinetically monitored using model-free and model-based approaches.

33 Model-based ARR reductions in younger patients (<=12 yea

34 CCV and FTE response contradict equilibrium model-based assumptions and warrant caution when assessi

35 ior with more precision by using trial-level model-based Bayesian computational modeling and probabil

36 t work has argued that people exhibit little model-based behavior unless it leads to higher rewards.

37 We found significant differences in these model-based biophysical parameters throughout the treatm

38 and suggest a critical role for human OFC in model-based but not model-free behavior.

39 ecies evidence for a critical role of OFC in model-based but not model-free control of behavior.SIGNI

40 e, which is necessary but not sufficient for model-based choice.

41 supervised approach to improving accuracy of model-based classifiers.

42 An extension to a model-based clustering algorithm is proposed using mixtu

43 es of diet quality were used as input into a model-based clustering analysis.

44 Using model-based computational analyses of fMRI recordings in

45 This model-based construct determines gaze and pupil dilation

46 re linked to the developmental trajectory of model-based control and a remodeling of frontostriatal c

47 of both strategies was shifted towards less model-based control in obese participants.

48 control as expressed in a phenotype of less model-based control potentially resulting from enhanced

49 etermine the dosing of such molecules with a model-based control strategy.

50 cated that obese participants relied less on model-based control than overweight and normal-weight pa

51 been framed computationally as a deficit in model-based control, and have been linked also to abnorm

52 escence there is a within-person increase in model-based control, and this is more pronounced in youn

53 However, the developmental trajectory of model-based control, including an interplay between its

54 esults reveal that ACC is a critical node in model-based control, with a specific role in predicting

55 profile of this developmental maturation in model-based control.

56 not quadratic, relationship between BMI and model-based control.

57 We conducted a model-based cost-effectiveness analysis to compare the c

58 Recently, model-based data integration has emerged as a means to a

59 Model-based decisions use predictions of the specific co

60 Finally, we use model-based decoding to show that the transition from li

61 ce package provided as a resource supporting modeling-based discovery in the community.

62 Both parameter recovery and the stability of model-based estimates were poor but improved substantial

63 ve ICP (nICP) was computed using a validated model-based estimation method.

64 e necessary to develop robust, reliable, and model-based experimental probes; recruit larger sample s

65 ous experiments in vivo, providing the first model-based explanation for this phenomenon.

66 Model-based exploration followed by experimental testing

67 t test sets, where the addition of structure model-based features improved AUC from 0.611 and 0.520 t

68 erived from protein sequences with structure model-based features, which are geometric information ex

69 Using model-based fMRI, we found that activity in the rostrola

70 On the neural level, computational modeling-based fMRI analyses revealed that 5-HT depletio

71 irth-death processes have given biologists a model-based framework to answer questions about changes

72 ional modelling of behaviour, model-free and model-based functional MRI, and effective connectivity d

73 y spatial correlation in prevalence within a model-based geostatistics framework.

74 provide a quantitative baseline to constrain model-based hypotheses of plant responses to eCO(2) unde

75 According to the second hypothesis, model-based imitation (MB), the learner infers the demon

76 d the 2-year follow-up, with 91.2% requiring model-based imputation.

77 We developed a consumer-resource model based in game theory that predicts the root densit

78 By combined pharmacophore-modeling-based in silico and fluorescence polarization-b

79 been constrained by the lack of a coherent, model-based inference framework.

80 interactions between the two regions support model-based inference.

81 A key model-based insight is that various decision thresholds

82 l-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrosp

83 rrents derived from observed hydrography and model based Lagrangian trajectories reveal zonal shifts

84 Here we use mouse tracking to study model-based learning in stochastic and deterministic (pa

85 Conversely, the model-based learning tasks induced a decrease in intraco

86 nt motor-learning tasks, i.e. model-free vs. model-based learning tasks, and their possible different

87 , i.e. a ballistic motor task, compared with model-based learning tasks, i.e. visuomotor-learning tas

88 cement of rewarded actions, and the other is model-based learning, which considers the structure of t

89 dopamine for learning and are thought to use model-based learning.

90 Here we present a metabolic model-based machine learning classifier, named Metabolic

91 ncreased information processing supported by model-based (MB) operations, including affective prospec

92 umented reliance on two separable systems: a model-based (MB) system and a model-free (MF) system.

93 (model-free; MF) learning and deliberative (model-based; MB) planning.

94 We augment the Kriging variance with model-based measures, for instance providing local sensi

95 We conducted mixed effect model-based meta-analyses evaluating incidence of anal S

96 ast and human data has showed that our mixed model-based method has similar performance with simple l

97 rs' type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum val

98 We used a model-based method to generate separate rest and stress

99 gulation (CA) have been developed, including model - based methods (e.g. autoregulation index, ARI),

100 estimate population structure using several model-based methods and infer the demographic history of

101 Both model-free and model-based methods are involved.

102 We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, w

103 We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux.

104 Goal-trackers instead exhibit a stronger model-based neural state prediction error signal.

105 tocol, SUV ratios (SUVRs) were compared with model-based nondisplaceable binding potential (BP (ND))

106 Model-based odds ratios for PVC risk reduction in 2-day

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

133 A mechanical model based on density functional theory calculations an

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

166 A model based on polymerizing actin filaments pushing agai

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

213 Modeling based on these structures suggested different p

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

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

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

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

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

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

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

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

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

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

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

225 Two prediction models based on colorimetric analysis allow estimation o

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

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

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

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

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

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

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

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

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

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

236 Models based on element predictors showed accuracies ran

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

238 The models based on feature extraction exhibited higher pred

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

277 vere NAFLD and fibrosis more accurately than models based only on clinical or bacterial data.

278 erise particle systems, which involve either model-based or numerical analyses.

279 on of these estimates were evaluated through model-based performance indices and graphical methods.

280 indings clarify both the neural mechanism of model-based planning and the scope of hippocampal contri

281 artificial intelligence techniques, in which model-based planning approaches have historically strugg

282 spatial memory, also plays a causal role in model-based planning.

283 Our model-based predictions correctly classify SNP effects i

284 ort of acute stroke patients (n = 101) using model-based predictions of cognitive deficits generated

285 tive calls by applying a stringent filter to model-based predictions, then rescores remaining candida

286 consistent with predictions from theoretical models based primarily on evidence from language compreh

287 ctivity (NPP) remote sensing product, an NPP model-based product and four gross primary productivity

288 We propose a new single-model-based QA method ResNetQA for both local and global

289 is necessary for inferring value in tests of model-based reasoning, including in sensory precondition

290 whole-brain fMRI adaptation and searchlight model-based representational similarity analysis, we fou

291 r tPA payments in acute ischemic stroke, our model-based results suggest financial incentives leading

292 p" (eg, having a spouse living with HIV), a "model-based" risk score constructed with logistic regres

293 We found that the model-based selection outperforms the conventional inclu

294 and that functional searchlight can improve model-based similarity and decoding accuracy.

295 ordingly, ACC is necessary only for updating model-based strategies, not for basic reward-driven acti

296 ectly classified 58% of seroconversions, the model-based strategy 68%, and machine learning 78%.

297 strategy targeted 42% of the population, the model-based strategy targeted 27%, and machine learning

298 We present a novel approach for model-based tumor subclonal reconstruction, called MOBST

299 Model-based tuning and decoding analyses revealed that p

300 l form for dynamic causal models-state space models based upon differential equations-that can be use