ライフサイエンスコーパス: dynamical model

1 mization principles using a simple two-state dynamical model.

2 menon by using field-derived parameters in a dynamical model.

3 ast observations of every nodal state to the dynamical model.

4 f the response, informing the structure of a dynamical model.

5 s as well as in silico data from a nonlinear dynamical model.

6 ely used conceptual framework via a stylized dynamical model.

7 described by a biphasic temporal filter and dynamical model.

8 of magnitude in stellar mass, using detailed dynamical models.

9 ar system, contrary to predictions of recent dynamical models.

10 but also their biological implications with dynamical models.

11 ting atmospheric dynamics and physics inside dynamical models.

12 diction skill of ENSO compared to individual dynamical models.

13 form conceptual cartoons into structural and dynamical models.

14 y abundant, which resolves the conflict with dynamical models.

15 NAO that exhibits higher skill than current dynamical models.

16 which is comparable to the skill of current dynamical models.

17 rived correlation functions based on several dynamical models.

18 obviates the need for parameterizing complex dynamical models.

19 be easily generalized to other open quantum dynamical models.

20 provides direction for future structural and dynamical models.

21 ference on the hidden states of hierarchical dynamical models.

22 otochemical reactions, which instead require dynamical modeling.

23 based quantum mechanics-molecular mechanics dynamical modeling.

24 r falsify the predictions of nanometer-scale dynamical modeling.

25 gorithm, Fourier analysis, and kinematic and dynamical modeling.

26 The dynamical model adjustment is performed via Approximate

27 Based on this dynamical model and additional data, such as known TF bi

28 In certain simple settings, dynamical modeling and conventional statistical methods

29 DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural-behaviora

30 alists learn the language of mathematics and dynamical modeling and theorists learn the language of b

31 Dissection of the fit dynamical models and closed-loop modeling with experimen

32 The combination of dynamical models and experiments has helped us unravel t

33 We used a combination of dynamical models and experiments to understand the condi

34 along the different directions using several dynamical models and find that hydrodynamic correlations

35 ic disease epidemiologists, the link between dynamical models and predominant causal inference paradi

36 y that can be generalized to a wide range of dynamical models and recording techniques and to other a

37 can enable real-time sparse reconstruction, dynamical modeling, and control of extremely unsteady gu

38 zed analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilit

39 The recent low skills of the dynamical models are attributed to deficiencies in captu

40 lished statistical prediction conflicts with dynamical models as they predict large, opposite, change

41 , at least twice as high as that of the best dynamical models available (0.26), indicating improved p

42 et plaque morphology in the context of a new dynamical model based on competing aggregation and disag

43 Here, we used a dynamical model based on empirical energy budget data to

44 ly test these theories, we develop a general dynamical model based on the theoretical framework of cu

45 From these bioinformatics and dynamical models based on experimental data, we conclude

46 In this study, we use dynamical-model based Bayesian inference to investigate

47 Using a dynamical model-based inferential framework, we find tha

48 t example, we show that the obtained reduced dynamical model can reproduce the full statistics of spa

49 -resolution time series data and data-driven dynamical modeling can uncover the dynamics and causalit

50 Dynamical models can be used to transparently encode com

51 Dynamical models can produce inner Solar System configur

52 While physics-based dynamical models can successfully forecast sea ice conce

53 The observations can be described by a dynamical model characterized by a single novelty factor

54 Dynamical models, commonly used in infectious disease ep

55 xperiments with a six-dimensional conceptual dynamical model confirm that these models capture key st

56 Analysis of the dynamical model confirms this prediction.

57 Here, we present a novel dynamical model consisting of two coupled populations of

58 Dynamical models consisting of networks of neural masses

59 ies coexistence and examine how the class of dynamical models constrain the topology of assembly grap

60 ween structurally coupled elements, and that dynamical modeling could be used to identify other damag

61 Altogether, dynamical models could guide development of more precise

62 tational framework that uses continuous-time dynamical models coupled with Bayesian dimensionality ad

63 We study a general dynamical model describing coevolution of two haploid po

64 We augment existing/low-fidelity dynamical models directly in their partial differential

65 Critically, only dynamical models displayed tensor structure that agreed

66 rred the first systems-biology comprehensive dynamical model explaining patterning in planarian regen

67 A minimal dynamical model explains the temporal pattern of phage e

68 iction of the TS activities that the current dynamical models fail.

69 of the methodology: (i) to obtain a reduced dynamical model for a polypeptide dynamics in water and

70 sted integral control, we arrive at a simple dynamical model for calcium homeostasis.

71 Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal

72 Here, we present a minimal dynamical model for the MJO that recovers robustly its f

73 gical significance of this trend, we drive a dynamical model for the population dynamics of the mosqu

74 Conceptual dynamical models for anisotropic turbulence are introduc

75 esenting causal systems and the relevance of dynamical models for causal inference.

76 Evolutionary dynamical models for cyclic competitions of three specie

77 In this commentary, we explain the use of dynamical models for representing causal systems and the

78 We learn novel multiscale dynamical models for spike-LFP network activity in monke

79 sual DNA structures, and their use to obtain dynamical models for this class of systems needs to be i

80 orthern France and also suggest that current dynamical model forecast systems have large potential fo

81 For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attribut

82 However, seasonal dynamical model forecasts for European summers have very

83 n approach that integrates DL forecasts with dynamical model forecasts.

84 Inferring dynamical models from data continues to be a significant

85 e identified challenges inherent to learning dynamical models from these snapshots and how our method

86 egression-based methods) are equivalent, but dynamical modeling has advantages over conventional stat

87 Different dynamical models have been proposed to represent transit

88 ear state-space models, also known as linear dynamical models, have been applied to model genetic net

89 This paper uses dynamical models, household power consumption, and photo

90 Most dynamical models, however, have limited skill in seasona

91 Using a Lotka-Volterra type population dynamical model, I then show that in such communities, i

92 lizes the replicator equation, a widely used dynamical model in evolutionary game theory.

93 forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice,

94 uncertainty and be adapted for use for other dynamical models in networks.

95 Here, using dynamical models in realistic aquatic metacommunity syst

96 Dynamical models in the form of systems of ordinary diff

97 orecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based

98 We used this tool to build a number of dynamical models, including a 130-protein large-scale mo

99 gorous maximum likelihood inference based on dynamical models incorporating multiple sources and outc

100 Dynamical modelling indicates that transmission risk wil

101 With process-based dynamical models informed by almost two decades of month

102 The dynamical models introduced here potentially provide a u

103 Furthermore, a dynamical model involving temperature is given for depic

104 The dynamical model is embedded into a Bayesian framework an

105 A dynamical model is presented as a framework for muscle a

106 Here, a simple dynamical model is used to approximate walking with a co

107 dictive capability and computational cost of dynamical models is often at the heart of augmenting com

108 s.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth

109 An important prediction of neural dynamical models is that previously observed neural acti

110 The advantage of such dynamical models is that they allow for the quantificati

111 findings highlight the immense potential of dynamical models, mathematics, and data-guided methodolo

112 tions (eg force fields or visual rotations), dynamical models may help to understand how joint coordi

113 ssing interventions-we highlight examples of dynamical modeling methods and advances in their applica

114 alling maneuvers with a 52-degree-of-freedom dynamical model of a bat to show that modulation of wing

115 We examine a nonreciprocally coupled dynamical model of a mixture of two diffusing species.

116 ehavioral models, which either incorporate a dynamical model of attentional focus, in the form of a h

117 Together, these findings support a new dynamical model of auditory word forms.

118 ion, and quantitatively agrees with a simple dynamical model of B cell differentiation.

119 nce and diminishing-returns epistasis into a dynamical model of changes in mean fitness over time.

120 ndividual bias, which is estimated through a dynamical model of choice.

121 quences in a chunking representation using a dynamical model of competing modes arranged to evoke hie

122 Here, we develop and analyze a dynamical model of CRISPR-mediated prokaryote-phage coev

123 e the parameter estimates thus obtained in a dynamical model of disease spread to show that extended

124 We show that the Peyrard-Bishop nonlinear dynamical model of DNA, which has been used to simulate

125 we present a new comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in

126 raction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit w

127 We present a simplified dynamical model of immune response to uncomplicated infl

128 The resulting dynamical model of independent replacements drawn from h

129 an modeling approach, combined with a simple dynamical model of influenza transmission, to estimate t

130 A spatial dynamical model of malaria transmission in the Lake Kari

131 We build on a minimal dynamical model of metabolic growth where the tension be

132 uitry in superior colliculus, we construct a dynamical model of neural activation that is modulated b

133 n(2+) ions bound, leading to a proposal of a dynamical model of P. fluorescens 07A metalloprotease ac

134 ty on incidence was also investigated with a dynamical model of poliovirus transmission to observe pr

135 We present a dynamical model of primate visual areas V1, MT, and MSTd

136 veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcr

137 function and use by developing a first-order dynamical model of stroke recovery with longitudinal dat

138 Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal trans

139 sites of plasticity are incorporated into a dynamical model of the cerebellar cortex and its interac

140 A dynamical model of the double-peaked emission lines cons

141 ically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic op

142 We developed a large-scale dynamical model of the macaque neocortex, which is based

143 Here, we use a nonlinear dynamical model of the moisture transport and recycling

144 In this article, we formulate a dynamical model of the olfactory transduction pathway, w

145 parent contradiction, we developed a minimal dynamical model of the simultaneous transmission of HIV

146 approaches to infer the network structure or dynamical model of the system.

147 , model-based methods focus on identifying a dynamical model of the system.

148 with parameters obtained from recent quantum dynamical modeling of experimental data assuming an expl

149 Dynamical modeling of gene regulation via network models

150 ing measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is i

151 s were found to decline with the radius, and dynamical modeling of the data indicates the presence of

152 Dynamical modelling of these data reveals the presence o

153 Dynamical modelling of within-household incidence showed

154 First, we used dynamical models of antibiotic resistance to predict the

155 ical steps in this approach are to construct dynamical models of biochemical reaction networks for la

156 rds the larger goal of developing predictive dynamical models of cellular behaviour.

157 Boolean networks are popular dynamical models of cellular processes in systems biolog

158 s the demand for tools to analyse stochastic dynamical models of chemical reactions.

159 theoretical constructs of metacognition onto dynamical models of decision uncertainty.

160 th the help of game dynamics, which includes dynamical models of evolution and individual learning.

161 Dynamical models of gene regulatory networks (GRNs) are

162 Studies were included if they used dynamical models of heterosexual HIV transmission, incor

163 that combining host behavioural traits with dynamical models of infectious disease scaled against ho

164 Our dynamical models of intrinsic theta-bursting neurons dem

165 We review dynamical models of lunar differentiation in the context

166 lly structured inputs as intrinsic dynamics, dynamical models of neural activity should account for m

167 We first show how training dynamical models of neural activity while considering be

168 These input-driven dynamical models of neural-behavioral data can uncover i

169 We show that for dynamical models of realistically structured ecological

170 this prediction, we built and characterized dynamical models of single-trial motor cortical activity

171 Quantitative and dynamical models of systems behaviors will supersede the

172 s of Solar System formation, consistent with dynamical models of terrestrial planet formation(11).

173 Dynamical models of the interaction between the planet a

174 b-based platform used to create and simulate dynamical models of various biological processes.

175 decision support tools based on mechanistic dynamical models offer an appealing solution due to thei

176 While developing a precise dynamical model on biological entity interaction is stil

177 cobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of

178 linear regression model derived solely from dynamical model output can skillfully predict observed a

179 We developed an approach in which dynamical model parameters and the population structure

180 e show remarkable fit to a 20-y-old MTT time-dynamical model predicting early trace intensity increas

181 these mechanisms in real time and extending dynamical model predictions after positive feedbacks act

182 Our dynamical model predicts that, even when present in low

183 This dynamical model promises to increase our quantitative un

184 n we evaluate to what extent three different dynamical models provide consistent predictions for the

185 Analysis of this dynamical model provided novel insights into the mechani

186 ophage development, we defined a qualitative dynamical model recapitulating cytokine-induced differen

187 Our dynamical model reproduces the spike time-dependent plas

188 Dynamical modeling revealed interlocking positive feedba

189 lex dynamics, empirically grounded nonlinear dynamical models should play a larger role in policy for

190 Dynamical models show that the high eccentricity is most

191 Dynamical modeling shows that these results can be expla

192 This paper presents a novel dynamical modeling solution to estimate the instantaneou

193 In order to analyse large complex stochastic dynamical models such as those studied in systems biolog

194 s behavior was reproduced in a multicellular dynamical model suggesting critical behavior in the isle

195 ciated with lymphomagenesis, requires robust dynamical modeling techniques.

196 the ATP hydrolysis kinetics, we construct a dynamical model that accounts for the stepwise processiv

197 approximate Bayesian computation to obtain a dynamical model that accurately predicts tissue patterni

198 This paper described a nonlinear dynamical model that allows for continuous changes in co

199 Based on these findings, we developed a dynamical model that captures interactions between audit

200 obust patterns across contexts and propose a dynamical model that closely reproduces empirical observ

201 s of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional,

202 A nonlinear dynamical model that incorporates the experimentally obs

203 Using a two-category dynamical model that integrates genomic and mortality da

204 Here we introduce a simple dynamical model that links these perspectives through on

205 We propose a dynamical model that reproduces this observation along w

206 We propose a simple quasilinear dynamical model that reproduces well the oscillation cha

207 We formalize this hypothesis with a dynamical model that reveals a strong analogy between be

208 We understand this process using a minimal dynamical model that simulates the overdamped dynamics o

209 Here, we show through a mechanism-based dynamical model that the diffusion of EMT-inducing signa

210 Dynamical models that assume that comets are not destroy

211 riability, whereas population ecologists use dynamical models that incorporate physical indicators as

212 We trained gene circuits, dynamical models that learn genetic architecture, on hig

213 e the accuracy and efficiency of a family of dynamical models that produce persistent copies.

214 among multiple regulators we could generate dynamical models that quantitatively account for the obs

215 lop an analytical learning method for linear dynamical models that simultaneously accounts for neural

216 spatial transcriptomes through a multiscale dynamical model to characterize multistability in space.

217 Here, we develop a statistical dynamical model to explain and quantitatively predict th

218 We developed a dynamical model to study the effects of phosphodiesteras

219 n tuberculosis from 1980 to to 2013 to fit a dynamical model to time trends in HIV prevalence, antire

220 - and low-transmission settings we applied a dynamical model to two unique adjacent cohorts of mesoen

221 iptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rate

222 Here, we use single-cell tracking and dynamical modeling to develop and validate a revised mod

223 onment facilitates the application of hybrid dynamical modeling to the reverse engineering of complex

224 propagated estimates and uncertainty through dynamical models to assess uncertainty in the epidemiolo

225 ely integrating the strengths of both DL and dynamical models to further improve ENSO prediction skil

226 ytes and used quantitative features to build dynamical models to investigate how regulation of actin

227 ent integration of sensory inputs by fitting dynamical models to PFC population responses of behaving

228 combines structural connectivity with local dynamical models to provide insight into the large-scale

229 st of the United States to that predicted by dynamical models undergoing different dispersal and envi

230 Dynamical modeling uniquely detects an outer nontransiti

231 The frequency correlation established the dynamical model used in the analysis, and it indicated t

232 nctional is a central component of continuum dynamical models used to describe phase transitions, mic

233 entify coherent groups of organoids and (iv) dynamical modeling using point distribution models to ex

234 ay to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (D

235 Using a dynamical model, we predict gene expression patterns for

236 Using a dynamical model, we propose that this mechanism optimize

237 he formulation and analysis of a multi-scale dynamical model, we show that the establishment of stabl

238 sed empirical model and the state-of-the-art dynamical models, we demonstrate that the WPSH is highly

239 rived from removal of nodes from the Boolean dynamical model were validated with experimental single

240 We propose a population dynamical model where immunity can be both acquired and

241 esults are specific features of a particular dynamical model, whereas others turn out to be quite rob

242 A dynamical model, which accounts for electrophysiological

243 Fitting dynamical models with an artificial intelligence algorit

244 used to combine high-dimensional, nonlinear dynamical models with observed data.

245 n models, or GCMs-that is, three-dimensional dynamical models with unresolved terms represented in eq

246 This paper develops a set of simplified dynamical models with which to explore the conditions un

247 the subseasonal capabilities of operational dynamical models, yet temperature and precipitation pred