ライフサイエンスコーパス: model-free

1 ational learning mechanisms, model-based and model-free.

2 between %B(I), %S, and previously calculated model free (13)C order parameters (S(2)) were observed.

3 it without estimating stochastic parameters (model-free adaptation processes, such as associative lea

4 The model-free affected-pedigree member method was used duri

5 Also, we applied a recently developed model-free algorithm called "step transition and state i

6 Model-free algorithms rely on the accumulation of substa

7 has been traditionally analyzed using either model-free algorithms, such as principal components anal

8 of 2.85; similar results were obtained with model-free analyses (maximum nonparametric linkage [NPL]

9 Targeted as well as model-free analyses convergently indicated that nucleus

10 Model-free analyses in both tasks showed the same two ef

11 of equilibrium peptide N(1)H/N(2)H exchange, model-free analyses of backbone NH relaxation data and r

12 the methyl rotation axes, were derived from model-free analyses of R(1) and R(2) data sets measured

13 Reduced spectral density mapping and model-free analyses reveal dynamic characteristics consi

14 or linkage in four families; model-based and model-free analyses showed a heterogeneity LOD (HLOD) of

15 and used the simple model-free and extended model-free analyses to fit the data and estimate the amp

16 Additional model-free analyses were conducted with the square root

17 ata than conventional model-free or extended model-free analyses with two or three correlation times.

18 Model-free analysis additionally demonstrated chemical e

19 Model-free analysis indicated that while substrate bindi

20 In addition, the model-free analysis indicates motions on the picosecond

21 An analysis of backbone dynamics based on a model-free analysis of 15N relaxation data, which incorp

22 Here, we demonstrate a novel approach to the model-free analysis of critical micellar concentrations

23 analytical and computational approach for a model-free analysis of metabolic data applicable to mamm

24 A model-free analysis of our relaxation data indicates sub

25 le, independently reproducing results from a model-free analysis of small-angle neutron and X-ray sca

26 Model-free analysis of the NMR relaxation parameters ind

27 ison of the order parameters obtained from a model-free analysis of the relaxation data with the B-fa

28 rates and (1)H-(15)N heteronuclear NOEs with model-free analysis of the results.

29 intensity synchrotron source, combined with model-free analysis of the scattering data, to demonstra

30 Model-free analysis shows that the catalytic residues in

31 Model-dependent and model-free analysis techniques provided converging evide

32 An extension of the Lipari-Szabo model-free analysis was used to determine the order para

33 A voxel-wise tracer kinetic model and a model-free analysis were applied to the dynamic MR data.

34 he curve for R1 as a function of field and a model-free analysis were used to extract tauc, a correla

35 a combination of NMR relaxation dispersion, model-free analysis, and ligand titration experiments to

36 s estimation of the enthalpy of melting by a model-free analysis, yielding DeltaHcal= 614 kcal mol-1.

37 re obtained for this complex by Lipari-Szabo model-free analysis.

38 Only 4 additional genes were positive in model-free analysis.

39 nd experimental data, not available from the model-free analysis.

40 zed by (15)N NMR relaxation measurements and model-free analysis.

41 d and fit to spectral density functions by a model-free analysis.

42 As determined by "model-free" analysis, RNase A is conformationally rigid

43 consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model

44 and of structured loops, and used the simple model-free and extended model-free analyses to fit the d

45 Because model-free and model-based approaches have different bia

46 ng task, which allows discrimination between model-free and model-based behavioural strategies.

47 oral expression and the neural signatures of model-free and model-based control.

48 trategies for reinforcement learning, called model-free and model-based learning.

49 ecisions using different strategies known as model-free and model-based learning; the former is mere

50 y to expectations, the signal reflected both model-free and model-based predictions in proportions ma

51 This distinction is well captured by model-free and model-based reinforcement learning algori

52 fference learning models, is compatible with model-free and model-based reinforcement learning, repor

53 show that the hallmark task for dissociating model-free and model-based strategies, as well as severa

54 obtains an accuracy-demand trade-off between model-free and model-based strategies.

55 ip between this architecture and learning in model-free and model-based systems, episodic memory, ima

56 relaxation data using both the Lipari-Szabo model-free and reduced spectral density function formali

57 his distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcemen

58 es were undertaken using both nonparametric (model-free) and parametric (model-based) methods.

59 s were undertaken using both non-parametric (model-free) and parametric (model-based) methods.

60 With the advantage of a model free approach and the power of probing multiple su

61 Analysis using the model free approach reveals that the protein is fairly r

62 three methods: the standard three-Lorentzian model free approach; the F(omega)=2omegaJ(omega) spectra

63 The Lipari-Szabo model-free approach (documented in a 1980 article in Bio

64 etermined that match those from the standard model-free approach applied to (15)N R1, R2 , and {(1)H}

65 Here we use a model-free approach based on measurement of many residua

66 Backbone dynamics were calculated using the model-free approach based on the (15)N relaxation rate c

67 Here, we propose PC-Relate, a model-free approach for estimating commonly used measure

68 Lastly, a simulation-based model-free approach for obtaining A is proposed.

69 ictions, our results suggest that a flexible model-free approach may be the most promising way forwar

70 However, despite the model-free approach of G(s), for the systems considered,

71 ckbone resonances were interpreted using the model-free approach of Lipari and Szabo.

72 yzed molecular dynamics simulations with the model-free approach of nuclear magnetic relaxation.

73 This model-free approach provides a robust and clinically rel

74 Using a "lean" version of the model-free approach S(2) order parameters can be determi

75 We show that the model-free approach strongly correlates with k for whole

76 This indicates a need for a model-free approach to interpret such data.

77 a recently established Bayesian inheritance model-free approach to meta-analyze the data.

78 A generalized model-free approach with a full treatment of the anisotr

79 It is shown how the "extended model-free approach" can be used to analyze (15)N backbo

80 and 60.8 MHz were analyzed with an extended model-free approach, and revealed low-frequency motions

81 NMR relaxation data were analyzed by the model-free approach, corrected for rotational anisotropy

82 In addition to our generalized, model-free approach, we have introduced a mathematical t

83 The two data sets were analyzed by using the model-free approach.

84 ed for all TM2e backbone N-H bonds using the model-free approach.

85 tein by (15)N-relaxation experiments using a model-free approach.

86 t magnetic fields were interpreted using the model-free approach.

87 We also compare our model with the "extended model-free" approach and discuss possible future develop

88 ears to be more accurate than other existing model-free approaches to estimating coalescent times.

89 gths and weaknesses of these model-based and model-free approaches, as well as difficulties associate

90 d using reduced spectral density mapping and model-free approaches.

91 We compare three model-free approaches: (1). nonparametric t-test, (2). W

92 Multipoint, model-free ARP linkage analysis was performed.

93 e high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be e

94 Model-free-based NMR dynamics studies have been undertak

95 arning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspec

96 nguish it from pre-determined, habitual, or "model-free" behavior.

97 In a canine model, free-breathing 3D radial UTE performs better than

98 Model-based RL is more flexible than model-free but requires sophisticated calculations using

99 whelming individual chemical identities, and model-free characterizations of chemically exchanging pa

100 cluded in previous models, were critical for modeling free chlorine loss.

101 sed dopamine levels promote model-based over model-free choice.

102 The model-free clustering analysis of the resultant protein

103 tes the contribution of model-based, but not model-free, contributions to behavior.

104 mply result from a shift from model-based to model-free control but is instead dependent on the inter

105 on task known to distinguish model-based and model-free control in humans.

106 In contrast, habit control, also known as model-free control, is based on an integration of previo

107 nts manifest a dominance of the less optimal model-free control.

108 man individuals' reliance on model-based and model-free control.

109 ted and habitual behavior as model-based and model-free control.

110 overemphasis on less flexible, maladaptive 'model-free' control systems.

111 nce for heritability of both model-based and model-free DD measures and suggests that DD is a promisi

112 herefore, we investigated model-based versus model-free decision making and its neural correlates as

113 ltiway (here, more specifically multilinear) model-free decomposition methods such as PARAFAC (parall

114 By giving model-free definitions of direct and indirect effects an

115 At each temperature, the model-free-determined order parameters, S(2), were signi

116 m 15N NMR relaxation data using the extended model free dynamics formalism.

117 These data were analyzed by using a model-free dynamics formalism to determine the generaliz

118 The extended model free (EMF) approach has been implemented to analyz

119 A model-free empirical analysis, comparing the binding str

120 Incorporating this mechanism into a model free energy for the bilayer, we find that between

121 Molecular modeling, free-energy perturbation calculations, X-ray c

122 parate mechanisms underlying model-based and model-free evaluation and support the hypothesis that mo

123 bulent convective flow and combine them with model-free, experience-based, reinforcement learning alg

124 We present unequivocal and model-free experimental proof for the presence of streng

125 ed rigorous theory-based deconvolution for a model-free extraction of the energy landscape and local

126 By use of a model-free extrapolation method, the transition temperat

127 The presented phasor analysis is model-free, fast and accurate.

128 nal dynamics by spectral density mapping and model-free fitting procedures.

129 usion chromatography for the isolation and a model-free fluorescence fluctuation analysis for the inv

130 re is a wide variety of comparatively simple model-free forecasting methods that could be used to pre

131 The relaxation data is analyzed using a model free formalism which takes into account the very h

132 pectral density mapping and the Lipari-Szabo model free formalism.

133 elaxation data according to the Lipari-Szabo model free formalism.

134 the complexity of motions, the commonly used model-free formalism could not be used to reflect the dy

135 The data were analyzed using a model-free formalism to determine generalized order para

136 Data are analyzed using the Lipari-Szabo model-free formalism to determine order parameters and t

137 etermined from the relaxation data using the model-free formalism while accounting for diffusion anis

138 500 and 750 MHz, whether interpreted by the model-free formalism with axial diffusion anisotropy or

139 ion times and interpretation of these by the model-free formalism with axial diffusional anisotropy f

140 entration sample were interpreted, using the model-free formalism, to provide insight into protein dy

141 ng of relaxation data using the Lipari-Szabo model-free formalism.

142 clear Overhauser effects were analyzed using model-free formalism.

143 roups were determined using the Lipari-Szabo model-free formalism.

144 750 MHz, and analyze these results using the model-free formalism.

145 ameters in the context of the Lipari & Szabo model-free formalism.

146 (R(ex)) were obtained using the Lipari-Szabo model-free formalism.

147 lyzed in the context of the Lipari and Szabo model-free formalism.

148 stematic effects of aggregation on the usual model-free formalism.

149 lyzed in the context of the Lipari and Szabo model-free formalism.

150 olomon-Bloembergen equation incorporating a "model-free" formalism, based on a multiple-structure rep

151 We present a model-free formulism based on the ratio of total areas u

152 lver nanoparticles is presented here using a model-free framework that derives the energy of critical

153 ity mapping approach and within the extended model-free framework.

154 n task, which has been shown to discriminate model-free from model-based control.

155 ity of exploiting it for constructing enzyme models free from aggregation equilibria, are discussed.

156 rom the Beaver Dam Eye Study and performed a model-free genome-wide linkage analysis for markers link

157 he Fibromyalgia Family Study and performed a model-free genome-wide linkage analysis of fibromyalgia

158 he Beaver Dam (WI) Eye Study and performed a model-free genomewide linkage analysis for markers linke

159 Phasor diagrams are model-free graphical representations of transformed time

160 of either mechanism, we show a bias towards model-free (habit) acquisition in disorders involving bo

161 sidered to arise out of contributions from a model-free habitual system and a model-based goal-direct

162 e approximations, discerning between innate, model-free, heuristic, and model-based controllers.

163 alue of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance m

164 ipoint heterogeneity LOD scores (HLODs) plus model-free identity-by-descent statistics and the multip

165 We use Q-learning as a model-free implementation of Temporal difference learnin

166 ng both model-driven seed-based analysis and model-free independent component analysis and controllin

167 istep decision task in which model-based and model-free influences on human choice behavior could be

168 Model-free internal dynamic analyses of these data provi

169 e results challenge the notion of a separate model-free learner and suggest a more integrated computa

170 ural data to argue that both model-based and model-free learners implement a value comparison process

171 he involvement of the dopaminergic system in model-free learning and prefrontal, central executive-de

172 Dopamine is widely thought to support model-free learning by modulating plasticity in striatum

173 ions linking striatal dopamine to putatively model-free learning did not rule out model-based effects

174 This favoring of model-free learning may underlie the repetitive behavior

175 fferent learning algorithms, model-based and model-free learning, respectively.

176 onal and model based and not consistent with model-free learning.

177 h work has pointed to a role for dopamine in model-free learning.

178 ions in striatal dopamine function relate to model-free learning.

179 found no effect of disease or medication on model-free learning.

180 prediction error signals thought to underlie model-free learning.

181 d their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospe

182 hypothesized to arise computationally from 'model-free' learning.

183 DESIGN, SETTING, AND PATIENTS: Model-free linkage analyses of 21 concordant-affected si

184 We applied model-based and model-free linkage analyses, as well as the pedigree dis

185 Model-free linkage analyses, using a dichotomous definit

186 Model-free linkage analysis identified one novel signifi

187 s and false-positive evidence for multipoint model-free linkage analysis of affected sib pair data.

188 hich OA segregates as a Mendelian trait, (2) model-free linkage analysis of affected sibling pairs, a

189 Model-free linkage analysis was followed by identificati

190 Model-free linkage analysis was performed, and tests of

191 A genome-wide model-free linkage analysis was performed, using two sem

192 Using multipoint model-free linkage analysis, we obtained a lod score of

193 satellite markers (average spacing 9 cM) and model-free linkage analysis.

194 family analysis of the data, performed using model-free linkage methods, suggests that there is evide

195 n it is applied to many nonparametric (i.e., model free) linkage results.

196 ellite markers: chromosome 8, with a maximum model-free LOD score of 2.2; chromosome 2, with a LOD sc

197 Model-free LOD-score methods are often employed to detec

198 ility distribution of the peak position in a model-free manner and compare the performance to manual

199 ectly interact can be used to evaluate (in a model-free manner) association/dissociation reactions of

200 In contrast, a model-free method based on state-space reconstruction ga

201 dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships

202 Although the model-free method is not universally applicable (for exa

203 We developed an automated, model-free method to rapidly and exhaustively examine th

204 Relaxation measurements were analyzed by the model-free method.

205 Linkage analyses were conducted using model-free methods (SIBPAL, S.A.G.E.) for AA, CA, and th

206 ence algorithms face one of two limitations: model-free methods are scalable but suffer from a lack o

207 es, can provide better forecasts than simple model-free methods for ecological systems with noisy non

208 s in which the underlying model is unknown, "model free" methods-such as affected sib pair (ASP) test

209 the "differences" between "model-based" and "model-free" methods and about which approach is better s

210 stochastic equivalence of "model-based" and "model-free" methods to be extended to multipoint analysi

211 So-called "model-free" methods typically simplify the underlying pr

212 nalyses were undertaken using nonparametric (model-free) methods.

213 The model free (MF) approach allows straightforward extracti

214 strategy and a fast, retrospective habitual model-free (MF) strategy.

215 Model-free multipoint linkage analysis strongly excluded

216 Here we define a model-free multipoint method on the basis of dense seque

217 Model-free, multipoint linkage analyses (SIBPAL2, SAGE v

218 Genomewide, model-free, multipoint linkage analyses were performed s

219 es of the labeled mutants were well fit to a model-free nonassociative biexponential equation.

220 ped with markers spaced by every 10 cM and a model-free nonparametric linkage (NPL-all) analysis was

221 Te in a laser-induced plasma (LIP), using a model free of assumptions regarding local thermodynamic

222 NOX4 inhibition was confirmed in an ex vivo model, free of vascular and BBB components.

223 ent with experimental data than conventional model-free or extended model-free analyses with two or t

224 s (parametric linkage) and complex diseases (model-free or non-parametric linkage).

225 directed versus habitual, model-based versus model-free or prospective versus retrospective.

226 omparable global tumbling times (tau(m)) and model-free order parameters (S(2)) under the two pH cond

227 ly significant elevations (P << .001) in the model-free parameter initial area under the curve and in

228 and side-chain NH groups and calculated the model-free parameters for R50A-rCMTI-V and R52A-rCMTI-V.

229 rs were interpreted in terms of Lipari-Szabo model-free parameters using anisotropic expressions for

230 mega = 0, omega(N), and 0.87omega(H) and the model-free parameters were evaluated from the experiment

231 The fitting of model-free parameters yielded S (2) variability which is

232 NMR relaxation data in terms of the extended model-free parameters.

233 possible to uniquely determine all "extended model-free" parameters without any a priori assumptions

234 In the present study, we introduce two new model-free parametric linkage tests, known as "MLOD" and

235 A model-free pharmacokinetic measurement based on area und

236 The model-free phase reveals inconsistencies within the data

237 y encountered imperfect crystals and enables model-free phasing.

238 Whereas model-free prediction error signals were preserved, alco

239 l prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum.

240 s in ventral striatum notably covarying with model-free prediction errors.

241 radigms, that these distinct model-based and model-free processes combine to learn an error-based mot

242 o fit the data for several residues with the model-free protocol revealed the presence of correlated

243 Our approach allows model-free quantitative analysis of the degrees of freed

244 Model-based and model-free reinforcement learning (RL) have been suggest

245 ed to signal prediction errors as defined in model-free reinforcement learning algorithms.

246 We then consider model-free reinforcement learning strategies that exploi

247 such learning is not easily accounted for by model-free reinforcement learning theories such as tempo

248 ached-value error signal proposed to support model-free reinforcement learning, cached-value errors a

249 wo computational mechanisms, model-based and model-free reinforcement learning, neuronally implemente

250 rbitration mechanism between model-based and model-free reinforcement learning, placing such a mechan

251 k promises to differentiate model-based from model-free reinforcement learning, while generating neur

252 dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signal

253 process can yield choice patterns similar to model-free reinforcement learning; however, samples can

254 Model-free relaxation analysis revealed picosecond to na

255 Decisions may arise via 'model-free' repetition of previously reinforced actions

256 ity of the ventral striatal BOLD signal as a model-free report.

257 the same residues show the plasticity in the model-free residual dipolar coupling (RDC) order paramet

258 Whereas model-free RL uses this experience directly, in the form

259 fied with slow, deliberative processing, and model-free RL with fast, automatic processing.

260 Model-free RNA structure comparisons were performed usin

261 describe a dynamic programming approach for model-free sequence comparison that incorporates high-th

262 owing estimation of recombination rates in a model-free setting.

263 Model-free solutions (e.g., soft models) are produced un

264 These data were analyzed using model-free spectral density functions and reduced spectr

265 d S(CD) order parameters are correlated by a model-free, square-law functional dependence, signifying

266 A novel model-free statistical approach (self modeling curve res

267 Findings were analyzed by 2 model-free statistical linkage procedures.

268 The method uses model-free statistics to identify peak-like distribution

269 Model-free strategies are computationally cheap, but som

270 ugh simulation that under certain conditions model-free strategies can masquerade as being model-base

271 tients were relatively better explained by a model-free strategy due to reduced inference on the alte

272 for two types of learning strategy: a narrow model-free strategy that learns an input-output mapping

273 In contrast, model-free subjects tend to ignore model-based aspects o

274 tomated "reverse engineering" approaches for model-free symbolic nonlinear system identification may

275 nation of a Model-Based system and a revised Model-Free system can account for the development of dis

276 ed or model-based system and the habitual or model-free system in the domain of instrumental conditio

277 Moreover, we show that revising a classical Model-Free system to individually process stimuli by usi

278 n error-like signal arising from a classical Model-Free system, necessary for Pavlovian conditioning.

279 berative "model-based" and a more reflexive "model-free" system.

280 deliberative "model-based" and a reflexive "model-free" system.

281 of control over behavior by model-based and model-free systems as a function of the reliability of t

282 the relative contribution of model-based and model-free systems during decision-making according to t

283 task and planning within it, to traditional model-free TD learning.

284 overlapping with those thought to carry out model-free temporal difference (TD) learning.

285 odel is too large to have a powerful, truly "model-free" test.

286 Model-free tests have been proposed.

287 and efficient likelihood-based analogues of "model-free" tests of linkage and/or linkage disequilibri

288 d, efficient, and powerful than traditional "model-free" tests such as the affected sib-pair, transmi

289 that this can be accomplished with a simple, model-free transformation that is general enough to be a

290 rning processes that can be characterized as model-free: use-dependent plasticity and operant reinfor

291 over, connectivity between these regions and model-free valuation areas is negatively modulated by th

292 bitration may work through modulation of the model-free valuation system when the arbitrator deems th

293 ue in our procedure does not directly accrue model-free value and further suggest that the cue may no

294 However, it has been suggested that model-free value might accrue directly to the preconditi

295 ng the view that dopamine transients reflect model-free value.

296 and the interaction between model-based and model-free values, prediction errors, and preferences is

297 In addition, our method provides model-free variable selection of important prognostic ma

298 -step reward-learning task which dissociates model-free versus model-based learning.

299 Using a bi-tensor model, free-water values were found to be increased in t

300 multipoint method (either "model-based" or "model-free") with the same robustness to marker-locus ge