ライフサイエンスコーパス: polynomial

1 ot topological invariants like the Alexander polynomial.

2 involving coefficients of its characteristic polynomial.

3 We represent each interaction using a small polynomial.

4 ion chromatogram baseline with a third-order polynomial.

5 ed on scaled discrete Tchebichef moments and polynomials.

6 r corneal surface were analyzed with Zernike polynomials.

7 uous variables was assessed using fractional polynomials.

8 omolecules from the basis of general binding polynomials.

9 expressed in a spectral basis of orthogonal polynomials.

10 year, a continuous variable using fractional polynomials.

11 rities explored via multivariable fractional polynomials.

12 aberrations of a virtual pupil using Zernike polynomials.

13 rem which models such polynomials by Hermite polynomials.

14 ity: cubic regression splines and fractional polynomials.

15 sorption is well described by a second-order polynomial (130 - 47 theta - 1250 theta(2)) kJ/mol, yiel

16 Examples are given of how polynomial algebra can be used for the model analysis.

17 an extension of the multivariable fractional polynomial algorithm was adopted.

18 Generally, polynomial and allometric models yielded adequate goodne

19 ing a generic sixth-order Landau free energy polynomial and calculate the energy barrier (EB) for dir

20 ss-catalytic systems have been designed with polynomial and exponential amplification that exhibit th

21 We evaluated 6 piece-wise polynomial and exponential decay models that used differ

22 Radial Basis Function (RBF) outperformed polynomial and linear kernel functions.

23 nhance the original approach by using direct polynomial and logistic approximations of oligonucleotid

24 ows the simplified construction of the Jones polynomial and medial graphs, and the steps in the const

25 e operators reduce the size of the resulting polynomial and thus the computational complexity dramati

26 The fitting polynomials and constraints have been constructed upon g

27 were selected using second-degree fractional polynomials and further modelled in a multilevel framewo

28 Data were analyzed with fractional polynomials and linear mixed effects models.

29 Tonography data were fitted to second order polynomials and values for the initial steady state IOP

30 tion functions, namely affine, second-degree polynomial, and third-degree polynomial, are effective f

31 The local polynomial approach has the ability to capture non-Gauss

32 roblem of infinite dimension to a problem of polynomial approximation employing tools from geometric

33 omeric and centromeric regions in which such polynomials are known to provide particularly poor estim

34 , second-degree polynomial, and third-degree polynomial, are effective for aligning pairs of two-dime

35 th prescribed Weibull function or orthogonal polynomials as input function.

36 Fractional polynomials assuming a skewed t distribution were used t

37 thods yield comparable results, although the polynomial-based approach is the most accurate in the we

38 d speed versus CRYSOL, AquaSAXS, the Zernike polynomials-based method, and Fast-SAXS-pro.

39 low from a general theorem which models such polynomials by Hermite polynomials.

40 A data-driven approach of arbitrary Polynomial Chaos (aPC) Expansion is then used to quantif

41 ction-diffusion model coupled with arbitrary Polynomial Chaos (aPC) to assess the impact of uncertain

42 Using polynomial chaos (PC) methods, we propagate uncertaintie

43 The proposed non-smooth polynomial chaos expansion (nsPCE) method is an extensio

44 n-Girard theorem and Viete's formulae to the polynomial coding of different aggregated isotopic varia

45 ed by significant regions (P<0.001) for each polynomial coefficient ranged from 0.2-0.9 to 0.3-1.01%

46 ct a single step GWAS (ssGWAS) on the animal polynomial coefficients for feed intake and growth.

47 A ssGWAS was conducted on the animal polynomials coefficients (intercept, linear and quadrati

48 We introduce polynomial collapsing operators for each subnetwork.

49 of the ciliary waveform were quantified from polynomial curves fitted to the cilium in each image fra

50 Ordinary differential equations (ODEs) with polynomial derivatives are a fundamental tool for unders

51 The inference is performed on the polynomial development of the potential and on the diffu

52 onymous changes (dN/dS) shows a second-order polynomial distribution with bidirectionality between sp

53 e number of elements in these autogenerating polynomials does not increase exponentially with increas

54 They can be represented as polynomial dynamical systems, which allows the use of a

55 give rise, through mass-action kinetics, to polynomial dynamical systems, whose steady states are ze

56 ithms are presented for the translation into polynomial dynamical systems.

57 The simple function of quadratic polynomial enabled to reveal the different character of

58 s the process was well fitted by a quadratic polynomial equation (R(2)=0.9367, adjusted R(2)=0.8226)

59 data obtained were fitted to a second-order polynomial equation using multiple regression analysis a

60 A second-order polynomial equation was generated to identify and predic

61 howed that oxygen consumption followed a 2nd polynomial equation whereas phenylacetaldehyde and o-qui

62 atios of SIO were adjusted by a second order polynomial equation.

63 e unique real and positive root of a quartic polynomial equation.

64 direct method for solving general systems of polynomial equations based on quantum annealing, and we

65 two-site PTM system as the solutions of two polynomial equations in two variables, with eight non-di

66 blem of finding all solutions to a system of polynomial equations over the finite number system with

67 Classically solving a general set of polynomial equations requires iterative solvers, while l

68 e this method using a system of second-order polynomial equations solved on a commercially available

69 Quadratic polynomial equations were developed to best fit the rela

70 Different polynomial equations were provided to calculate pressure

71 roblems and Diophantine equations, which are polynomial equations with integer coefficients and integ

72 s, whose steady states are zeros of a set of polynomial equations.

73 ng the diffusion equation through a Legendre polynomial expansion.

74 s were described using a sixth-order Zernike polynomial expansion.

75 the economical use of memory attained by the polynomial expansions makes the study of models with fou

76 of quadrature angles, the order of Legendre polynomial expansions, and coarse and fine mesh grid.

77 onary process is found by means of truncated polynomial expansions.

78 The product of these polynomials express different scenarios when a signal ca

79 r instance, an "invariant" of a network is a polynomial expression on selected state variables that v

80 Polynomial extrapolation of all the data to zero denatur

81 Low-order polynomial fits to the model output spatial fields as a

82 A second-order polynomial fitted the experimental data (R(2): 0.9736; p

83 fuzzy optimal associative memory (FOAM), and polynomial fitting (PF), were evaluated with high perfor

84 odel for near-Gaussian distributed subpeaks, polynomial fitting for highly asymmetrical peaks, and pa

85 Polynomial fitting is a conventional baseline correction

86 h a mathematical modeling approach utilizing polynomial fitting.

87 bove quantitative variables and second-order polynomial fitting.

88 Zernike polynomials fitting was used to quantify the 3D distribu

89 with restricted cubic splines and fractional polynomials for nonlinear trends, to investigate the ass

90 is equivalent to the hyperbolicity of Jensen polynomials for the Riemann zeta function [Formula: see

91 data (x,y) of shapes were fit to third-order polynomials for two sessions, sides, and methods (predic

92 computational protocol applying the binding polynomial formalism to the constant pH molecular dynami

93 Fractional polynomials (FPs) were utilised to fit continuous variab

94 zed over a 3-mm and 5-mm pupil using Zernike polynomials from third-sixth order.

95 taldehyde and o-quinone were best fit with a polynomial function containing quadratic terms.

96 analysis is used to determine a two-variable polynomial function for each region to relate a voxel's

97 means the running time of the algorithm is a polynomial function of the length of the input.

98 ed as a continuous variable using a specific polynomial function to model the shape and form of the r

99 Secondly, a polynomial function was used to model the non-linear dat

100 of pure varieties was used to build a cubic polynomial function with R(2)=0.998.

101 residues were described with a second degree polynomial function.

102 xpression of the chemical shifts in terms of polynomial functions of interatomic distances.

103 ght were evaluated by using fitted nonlinear polynomial functions on bootstrapped samples.

104 tor of response variables based on published polynomial functions that described the relationship bet

105 ne the structure of Leavitt path algebras of polynomial growth and discuss their automorphisms and in

106 many-electron Schrodinger equation is a 'non-polynomial hard' problem, owing to the complex interplay

107 We show that for the blackbox polynomial identity testing (PIT) problem it suffices to

108 wton polytope of the partition function as a polynomial in energy parameters.

109 nal complexity T of typical networks that is polynomial in N.

110 tational geometry in that our algorithms are polynomial in nature and thus faster, making pairwise co

111 nges from strongly exponential to high-order polynomial in system size.

112 the problem to the space of individuals and polynomial in the more significant space-the methylated

113 al supply within a number of periods that is polynomial in the number of goods and 1/epsilon.

114 Its run time is polynomial in the number of particles, their energy, and

115 l requires classical computational resources polynomial in the system size, and very little overhead

116 m of at most k(n) k-th powers of homogeneous polynomials in C[x0,x1,...,x(n)].

117 , we use the Sum of Squares decomposition of polynomials in order to compute an upper bound on the wo

118 nomials slightly out-performed second-degree polynomials in these results, but second-degree polynomi

119 on (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), s

120 n (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and part

121 Next, we trained SVM models with polynomial kernel and obtained accuracy of 76.0%, AUC 0.

122 machines using the radial basis function and polynomial kernel function, we found that the predictabi

123 mples and utilize it to test both linear and polynomial kernels for predicting ZF protein-DNA binding

124 both normal and cancer cells, we formulate a polynomial likelihood to estimate the population genetic

125 ptible cultivars) absorbed Ca in a quadratic polynomial manner with increasing CaCl2 concentration fr

126 human urine samples suggest that low-degree polynomial mapping functions out-perform affine transfor

127 ge implementation of the recently introduced polynomial method for calculating the aggregated isotopi

128 This polynomial method needs less computer storage than the i

129 are compared with BRAIN, a recently reported polynomial method.

130 were analysed by use of linear or fractional polynomials mixed models adjusting for all available pot

131 thodology analysis results depicted that the polynomial model (second-order) can be used to predict r

132 The data was fitted in a second-order polynomial model and the parameters were optimized to en

133 By using the polynomial model of Hindmarsh and Rose (Proceedings of t

134 Response surface modeling using a cubic polynomial model of the bootstrapped sPLS-DA average pre

135 intensities were best explained by a quartic polynomial model of the prediction error.

136 A second-order polynomial model satisfactorily fitted the experimental

137 The fractional polynomial model showed a log-linear relation between eG

138 and correlation showed that the second-order polynomial model was appropriate to fit experimental dat

139 d Doehlert design, an empirical second-order polynomial model was developed for the total yield of: (

140 A method and a well-predictive, second order polynomial model was developed using multiple regression

141 A second polynomial model was generated and optimised reaction co

142 mental results were fitted to a second-order polynomial model where regression analysis and analysis

143 data could be fitted well into second-order polynomial model with the coefficient of determinations

144 which we derived a multivariable fractional polynomial model.

145 Using a polynomial modeling approach, OCT inner ring retinal thi

146 ed by using linear regression and fractional polynomial modeling.

147 parameters were calculated using fractional polynomial modelling.

148 Employing polynomial models and an empirical Bayes approach, we es

149 a were satisfactorily fitted to second-order polynomial models by multiple linear regression.

150 at the obtained by design of experiments the polynomial models of each extraction criteria were relia

151 Our algorithm uses polynomial models to describe the gene expression patter

152 Furthermore, polynomial models were used to predict the production of

153 predictions and adequacy of the second-order polynomial models.

154 on were analysed using multilevel fractional polynomial models.

155 Using fractional polynomials, modest curvilinear mortality increases (ran

156 Second-degree polynomial multivariable analysis showed a continuous no

157 Spline and polynomial networks form attractive alternatives to MI i

158 A key feature of our proposal is to encode a polynomial ODE system into a finitary structure akin to

159 erpretability of the aggregation is shown on polynomial ODE systems for biochemical reaction networks

160 d as primary variables, to fit a 5(th) order polynomial of the contour angle.

161 ctly related to the volume V through a cubic polynomial of the energy term PV with three fitting para

162 icity of all but finitely many of the Jensen polynomials of each degree.

163 zed to take advantage of higher-order binary polynomial optimizers.

164 rful amplification cascades that can achieve polynomial or exponential amplification of input signals

165 problems that require algorithms which take polynomial or exponential time.

166 We propose the use of polynomial or spline regression models as an alternative

167 ther ideas such as permutationally invariant polynomials or sums of environment-dependent atomic cont

168 3D shape and electrostatic expansions up to polynomial order L=30 on a 2 GB personal computer.

169 shed method, featuring an improvement of one polynomial order of computational complexity (to quadrat

170 The polynomial order P = 2 (third order accurate) was found

171 Golay (SG) derivative, smoothing points, and polynomial order, and extended multiplicative signal cor

172 in a random regression model using Legendre polynomials (order=2) and a relationship matrix that inc

173 conducted for a Reynolds number of 1600 for polynomials orders from P = 2 to P = 6.

174 of certain "universal models," with at most polynomial overhead.

175 Namely, we show that a general homogeneous polynomial p in C[x0,x1,...,x(n)] of degree divisible by

176 ns such as linear, quadratic or higher order polynomial patterns.

177 ynomials in these results, but second-degree polynomials performed nearly as well and may be preferre

178 gnated "BBB Score", composed of stepwise and polynomial piecewise functions, is herein proposed for p

179 day based on visual inspection of fractional polynomial plots of the association between ESI and indi

180 linear (r(2) range, 1.7 x 10(-6) to 0.99) or polynomial (r(2) range, 0.09 to 1.0) regression analysis

181 All responses were parameterized well by polynomials (R(2) values between 0.985 and 0.999), demon

182 Polynomial regression analyses revealed significant inve

183 nce of myopia over time was estimated, and a polynomial regression analysis was performed to assess s

184 Univariate and multivariate polynomial regression curves were fit, and the optimum p

185 Applying multiple polynomial regression enabled us to build phenomenologic

186 e days, with no discontinuities in the local polynomial regression for readmission at the 30-day mark

187 rformed automated background subtraction and polynomial regression for the quantification of a latera

188 experimental results show that the quadratic polynomial regression is the optimal mining model for es

189 The best model was a polynomial regression model of the natural log transform

190 DR was best represented using a third degree polynomial regression model, including age and optic dis

191 is proposed using mixtures of mixed effects polynomial regression models and the EM algorithm with a

192 e did analyses with second-degree fractional polynomial regression models in a multilevel framework a

193 Linear and fractional polynomial regression models were used to evaluate the r

194 nse relations were examined using fractional polynomial regression models.

195 ere was a high correlation (R(2)=1.0) with a polynomial regression of Y=-0.227X(2)+0.331X-0.001.

196 proposed a modeling approach based on local polynomial regression that uses climate, e.g. temperatur

197 itask regression to structurally regularized polynomial regression to detect epistatic interactions w

198 Polynomial regression was used to model the influence of

199 Tests for nonlinearity using polynomial regression were significant for several estro

200 Age-related changes were modeled using polynomial regression with sliding window methods, and m

201 tivariate Adaptive Regression Splines, local polynomial regression) were applied if >30% of samples w

202 cantly associated with elevation (orthogonal polynomial regression).

203 for pediatric blood pressure data, including polynomial regression, restricted cubic splines, and qua

204 e relationship and fit the curve using local polynomial regression.

205 count monthly means were also examined with polynomial regression.

206 macaque antibody responses were analyzed by polynomial regression.

207 el (length: 12.9 km), and applied linear and polynomial regressions to obtain the fossil fuel end-mem

208 s, the correlations (R(2)) from second-order polynomial regressions were 0.944 for log(10) HIV-1 RNA

209 lysis, predicted child growth curves through polynomial regressions] and advanced regression analyses

210 to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art

211 method has been introduced for refining the polynomial response surface model.

212 At last a polynomial retrieval algorithm is introduced.

213 cating the potential of this computationally polynomial scaling technique to tackle current solid-sta

214 g a comprehensive sensitivity analysis using polynomial SFs with varying orders and coefficients.

215 ia general linear models based on orthogonal polynomials showed similar responses in clinical paramet

216 In this study, we propose polynomial-size integer linear programming (ILP) formula

217 Third-degree polynomials slightly out-performed second-degree polynom

218 s were constructed using nonparametric local polynomial smoothing regressions.

219 ling and data quality measures, LOESS (local polynomial) smoothing of RT values, segmentation of data

220 r the two-way admixture model and proposed a polynomial spectrum (p-spectrum) to study the weighted S

221 We used a fractional polynomial spline regression analysis to assess the line

222 , adding age and BP to the analyses as cubic polynomial splines to model potential nonlinear relation

223 ctions can be well approximated by low-order polynomial structures (e.g., linear, quadratic).

224 The polynomial SVM outperforms previously published predicti

225 Using simple regression with a second-degree polynomial term, a model was fit to describe the relatio

226 Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of

227 ents that incorporated covariate predictors, polynomial terms for age, and product interaction terms

228 We estimated associations using polynomial terms in spatial error models adjusted for to

229 ivariable adjusted analyses with appropriate polynomial terms of alcohol consumption.

230 n eGFR and ESRD was modeled using fractional polynomial terms.

231 None of the 43 fractional polynomials tested provided a better fit to the data tha

232 The highest order polynomial that was tested (P = 6) was found to have the

233 generally fit with phenomenological binding polynomials that are underdetermined.

234 d muscles were captured using autogenerating polynomials that expanded their optimal selection of ter

235 Using these polynomials, the entire forearm anatomy could be compute

236 Scheimpflug images and expressed as Zernike polynomials through the sixth order over a 6-mm diameter

237 neal topograms and were expressed as Zernike polynomials through the sixth order.

238 structure factors are bounded-error quantum polynomial time ([Formula: see text])-hard for general l

239 ther polynomial time equals nondeterministic polynomial time (i.e., P = NP) is one of the hardest ope

240 well as avoiding issues of Non-deterministic Polynomial time (NP)-completeness associated with graph

241 in general are usually in non-deterministic polynomial time (NP)-hard complexity class.

242 en a set of called deletions, we also give a polynomial time algorithm for computing the critical reg

243 We also present a polynomial time algorithm for finding a near-optimal sup

244 Polynomial time algorithms have been proposed for restri

245 Solutions can be produced in polynomial time and are proven to be asymptotically boun

246 ve developed a greedy algorithm that runs in polynomial time and guarantees an O(ln n) approximation.

247 This algorithm is known to run in polynomial time and therefore can scale well in high-thr

248 The famous question whether polynomial time equals nondeterministic polynomial time

249 n RNA sequence is derived and implemented in polynomial time for both structurally ambiguous and unam

250 tage of our new algorithm is that it runs in polynomial time in the number of gene lineages if the nu

251 c mechanisms that allow evolution to work on polynomial time scales.

252 the sequence alignment problem, which has a polynomial time solution, the structural alignment probl

253 provably determine unique correspondences in polynomial time with high probability, even in the prese

254 Our alpha-Rank method runs in polynomial time with respect to the total number of pure

255 y, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial tim

256 uter science (NP stands for nondeterministic polynomial time).

257 ASTRAL runs in polynomial time, by constraining the search space using

258 P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly

259 eemed efficient if it can solve a problem in polynomial time, which means the running time of the alg

260 We compared two methods: the Multivariate Polynomial Time-dependent Genetic Association (MPTGA) me

261 -specific effects, we applied a Multivariate Polynomial Time-dependent Genetic Association (MPTGA) me

262 teleportation scheme for fixed dimension in polynomial time.

263 em as a linear least squares and solve it in polynomial time.

264 nd show that it enables evolution to work in polynomial time.

265 nown maximum flow problem and thus solved in polynomial time.

266 all problems in NP and #P could be solved in polynomial time.

267 could exactly simulate chemical reactions in polynomial time.

268 vel algorithm CCR capable of solving CCCP in polynomial time.

269 tion dynamics cannot find the unique peak in polynomial time.

270 en for easy problems known to be solvable in polynomial time.

271 -dimensional Ising ring and nondeterministic polynomial-time (NP) hard instances.

272 matical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe

273 (node counts, in general), and that no known polynomial-time algorithm exists in deciding if two grap

274 r such an infinite-sites model, we present a polynomial-time algorithm to find the most parsimonious

275 Hopper realizes the optimal polynomial-time approximation of the Hausdorff distance

276 We show that a polynomial-time computable, [Formula: see text]-degree h

277 mization for DNA shuffling) approach employs polynomial-time dynamic programming algorithms to select

278 The above polynomial-time exact algorithm and the linear-time appr

279 of these are classified as non-deterministic polynomial-time hard and thus become intractable to solv

280 We prove this is a nondeterministic polynomial-time hard problem and derive an approximation

281 combinatorial optimization problems through polynomial-time mapping.

282 We present FastMulRFS, a polynomial-time method for estimating species trees with

283 ontact maps of their interfaces: it produces polynomial-time near-optimal alignments in the case of m

284 was used in combination with a seventh-order polynomial to calculate five binding constants for each

285 cence background was estimated, by fitting a polynomial to each spectrum, and then subtracted.

286 s to use the partial hsg and its annihilator polynomial to efficiently bootstrap the hsg exponentiall

287 We used fractional polynomials to model continuous blood eosinophil counts.

288 he authors has shown that global, low-degree polynomial transformation functions, namely affine, seco

289 tched peaks suggests that global, low-degree polynomial transformations outperform the local algorith

290 Out-of-sample predictions using homothetic polynomials validated the indifference curves.

291 Use of a second-order polynomial venous-to-arterial transformation was robust

292 erent rules than standard computability; the polynomial vs. exponential time divide of modern computa

293 Quadratic polynomial was the best fitted mathematical model for th

294 nded multiplicative signal correction (EMSC) polynomial were investigated as preprocessing techniques

295 is approach is based on building third-order polynomials which are used to interpolate recombination

296 x loop a knot invariant called the Alexander polynomial whose degree characterizes the topology of th

297 ackbox PIT for 6,913-variate degree-s size-s polynomials will lead to a "near"-complete derandomizati

298 rouwer degree, and it creates a multivariate polynomial with parameter depending coefficients.

299 Cox regression and multivariable fractional polynomials with backwards elimination.

300 sion model, and a method based on fractional polynomials with which to estimate a suitable functional