ライフサイエンスコーパス: nonlinear model

1 tween competing models (e.g., a linear vs. a nonlinear model).

2 bed through an exploratory induced-charge EP nonlinear model.

3 n to temporal contrast using a simple linear-nonlinear model.

4 xtinction, is highly predictable by a simple nonlinear model.

5 fertilization and irrigation, and an updated nonlinear model.

6 tering media, justifying the use of complex, nonlinear models.

7 atios (ORs) were estimated using generalized nonlinear models.

8 ls were estimated by fitting distributed lag nonlinear models.

9 d additive mixed models with distributed lag nonlinear models.

10 inear models, and further improves with deep-nonlinear models.

11 of Q10 values estimated from both linear and nonlinear models.

12 g such a code, we estimated 1D and 2D linear-nonlinear models.

13 ration methods for the purposes of comparing nonlinear models.

14 function were analyzed using distributed lag nonlinear models.

15 ase-crossover design using a distributed lag nonlinear model (0- to 6-day lag) was used to estimate s

16 t a predictive control system coupled with a nonlinear model able to optimize the level of policies t

17 We find that linear-nonlinear models accurately predict navigational decisio

18 Overall, weighted parametric nonlinear models allowed us to compute Z score equations

19 This review describes evidence in support of nonlinear models and functional roles of TGF-beta signal

20 stantially lower than predicted by linear or nonlinear models and strictly observed for neurons with

21 e show that cross-tissue annotation requires nonlinear models and that the performance of scTab scale

22 an temperature incorporating distributed lag nonlinear models and the second stage pooling the estima

23 hen escalating from linear models to shallow-nonlinear models, and further improves with deep-nonline

24 mulated step responses of the linearized and nonlinear models are close and in good agreement with ex

25 veform can be represented efficiently with a nonlinear model based on a population spike code.

26 ciations were examined using distributed-lag nonlinear models based on Cox models.

27 It replaces the classic nonlinear, model-based optimization with a linear approx

28 When comparing the nonlinear models, both early-onset and late-onset groups

29 nses cannot be explained by linear or linear-nonlinear models but are well explained by a biophysical

30 l identifiability of a very general class of nonlinear models by extending methods originally develop

31 the extreme low-data regimen, training small nonlinear models by using only 45 chest radiographs yiel

32 a more moderate data regimen, training small nonlinear models by using only 528 chest radiographs yie

33 Additionally, linear-nonlinear models can predict behavioral responses based

34 Nonlinear models confirm that internalizing symptoms wer

35 s cross-sectional study used distributed lag nonlinear models, controlling for seasonality and long-t

36 The linear and nonlinear models corresponded to relative risks of 0.96

37 The optimization of nonlinear models demonstrates a powerful method for prog

38 A nonlinear model did not fit the data better than a linea

39 for summer mortality with a distributed lag nonlinear model (DLNM) for temperature (up to lag 3) and

40 st error structure arises naturally from the nonlinear model dynamics.

41 Distributed lag nonlinear models evaluated the associations of temperatu

42 We developed a nonlinear model explaining these GVS-evoked afferent res

43 ction procedure is developed for determining nonlinear model explanatory variables when they are know

44 h location with time-varying distributed lag nonlinear models, expressed through an interaction betwe

45 istent with previous studies that describe a nonlinear model for the dose-response of EEG parameters

46 We describe a nested, nonlinear model for the sum of metal-free and metal-cata

47 significant inverse associations emerged in nonlinear models for fruits (Pnonlinearity<.001) and veg

48 stics, we developed and evaluated linear and nonlinear models for predicting inactivation rate consta

49 fits of linear models and several different nonlinear models for the relationship of estimated rate

50 c effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome.

51 Using a nonlinear modeling framework applied to extracellular da

52 In a linear-nonlinear modeling framework, the spatiotemporal organiz

53 as a function of molecular size (volume), a nonlinear model had to be used, and a linearized biexpon

54 ilizer data generate underestimation bias in nonlinear models, high-resolution N application data are

55 ne-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) an

56 to assess associations, and distributed lag nonlinear models identified sensitive exposure windows.

57 ill enable extensive use of large linear and nonlinear models in systems biology and other applicatio

58 Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical f

59 Distributed-lag nonlinear modeling integrated in quasi-Poisson regressio

60 Both forms of bias are universal to nonlinear models (irrespective of consumer dependence) a

61 However, the process of solving these nonlinear models is computationally expensive.

62 We used nonlinear modeling (locally weighted scatterplot smoothi

63 Linear and nonlinear models obtained using quantum mechanical calcu

64 We then propose a nonlinear model of accumulation of evidence that account

65 This paper proposes a novel nonlinear model of cascade failure in weighted complex n

66 igh-resolution activity data and adoption of nonlinear model of N(2) O emission for capturing croplan

67 ng these coupling coefficients, we analyze a nonlinear model of positive feedback between ligand rele

68 A nonlinear model of serum 25(OH)D as a function of total

69 We combine our earlier nonlinear model of the energy-biomass balance in undrugg

70 es the order of the experimentally validated nonlinear model of the fan-induction motor system accoun

71 A nonlinear model of the phase-dependent effects of optica

72 Both linear and nonlinear modeling of balance data showed that a calcium

73 neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk va

74 siology Index was created using parsimonious nonlinear modeling of heart rate, mean arterial pressure

75 refrigeration techniques with comprehensive nonlinear modeling of the cQED sensor operation.

76 Nonlinear modelling of GSIS showed that response time wa

77 Nonlinear models of aging fit the data significantly bet

78 Both linear and nonlinear models of aging were tested.

79 acco use, including: (1) the need for novel, nonlinear models of population-based disease control; (2

80 ative data-driven modeling approach to learn nonlinear models of the coherent structures, approximati

81 Group linear and nonlinear models of the effect of disease duration on HA

82 mical (rate-constant based) and engineering (nonlinear) models of antibody expression to experimental

83 substantially better than traditional linear-nonlinear models on data from primary and non-primary au

84 bruptly increase at a threshold temperature (nonlinear model) or increase steadily with temperature c

85 le exponential decay model using Generalized Nonlinear Model (package gnm) in R.

86 We overcome these problems by combining nonlinear model predictive control with a novel adaptive

87 and exit strategies are determined based on nonlinear model predictive control, constrained to publi

88 exas, Florida, and the Eastern Seaboard; the nonlinear model predicts concentration in a geographic b

89 Moreover, nonlinear models provide better fit for structural-funct

90 d that coefficients of determination for our nonlinear models ranged from 0.393 to 0.582 in both data

91 Linear decomposition of this highly nonlinear model resulted in the identification of distin

92 Nonlinear models revealed that each modality exhibits un

93 The nonlinear modeling strategy is further divided into two

94 Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural N

95 presentation and generate a multidimensional nonlinear model that captures the dynamics of a SM netwo

96 mortality in Europe using a distributed lag nonlinear model that incorporates humid heat and compoun

97 then reconcile, these inconsistencies with a nonlinear model that incorporates observed asymmetries i

98 nses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus

99 Here we report linear and nonlinear modelling that shows that dust-gas interaction

100 he peak is incompatible with both linear and nonlinear models that attribute the cycle to eccentricit

101 observations exclude a broad class of linear-nonlinear models that have been proposed to describe dir

102 for building decoders, including structured, nonlinear models, the explicit incorporation of limb sta

103 systems produced linear models or required a nonlinear model to be provided manually.

104 as performed on the physiologically detailed nonlinear model to find appropriate complexity (Akaike i

105 of water chemistries were complemented with nonlinear modeling to quantify the rate and extent of eD

106 Using linear-nonlinear modeling to remove the contribution of the sti

107 We used quasi-Poisson distributed lag nonlinear models to estimate the association of mortalit

108 we address these challenges using flexible, nonlinear models to identify the factors that underlie r

109 conjunction with more powerful, potentially nonlinear models to improve the power of standard family

110 However, application of nonlinear models to individual patient courses (as oppos

111 We used two-stage distributed lag nonlinear models to quantify the interrelationships betw

112 -Poisson regression, using a distributed lag nonlinear model, to estimate heat-health associations.

113 Quasi-poisson regression and distributed lag nonlinear models up to 12 d were used, adjusting for dai

114 The nonlinear model uses three basic characteristics of the

115 istic regression models with distributed lag nonlinear models using lag 0 to 1 (immediate) and 2 to 6

116 tex V1 neurons into the elements of a linear-nonlinear model (via spike-triggered covariance analysis

117 buckling of the central joint, a mechanical nonlinear model was developed, introducing the concept o

118 The distributed lag nonlinear model was used to establish the association of

119 ed effects of temperature, a distributed lag nonlinear model was used.

120 interpreted in terms of a single-compartment nonlinear model, we concluded that Rp is predominantly d

121 e, multimodal MRI, and using cross-validated nonlinear modeling, we demonstrate that developmental br

122 For nonlinear models, we calculated confidence intervals (CI

123 ing a time-series design and distributed lag nonlinear models, we estimated the relative risk (RR) of

124 ogistic regression model and distributed lag nonlinear model were used to estimate the associations o

125 Similarly, the performance of linear and nonlinear models were compared in each dataset.

126 atients aged newborn to 14 years, linear and nonlinear models were fit to estimate FBC from age.

127 Three types of nonlinear models were fit, with no nonlinear association

128 Linear and nonlinear models were fitted to each dataset and measure

129 The best predictors of soil respiration in nonlinear models were the Normalized Difference Vegetati

130 on a convolutional neural network (CNN) as a nonlinear model which can handle high-dimensional fluid

131 Finally, an instructive statistically nonlinear model with many degrees of freedom, mimicking

132 When estimated in a nonlinear model with the full sample, IQ declined by 7.4

133 In black adults, the association was nonlinear; models with cubic splines suggested evidence