ライフサイエンスコーパス: polynomial

1 ion chromatogram baseline with a third-order polynomial.

2 nt before extinction, is encoded in the risk polynomial.

3 involving coefficients of its characteristic polynomial.

4 We represent each interaction using a small polynomial.

5 uous variables was assessed using fractional polynomials.

6 omolecules from the basis of general binding polynomials.

7 expressed in a spectral basis of orthogonal polynomials.

8 year, a continuous variable using fractional polynomials.

9 rities explored via multivariable fractional polynomials.

10 aberrations of a virtual pupil using Zernike polynomials.

11 ed on scaled discrete Tchebichef moments and polynomials.

12 eal topographies was decomposed into Zernike polynomials.

13 rm is given by the ratio of two third-degree polynomials.

14 of Gegenbauer (also known as ultraspherical) polynomials.

15 et of Schur functions, which are homogeneous polynomials.

16 r corneal surface were analyzed with Zernike polynomials.

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

18 on is well described by another second-order polynomial (174 - 700 theta + 761 theta(2)) kJ/mol.

19 scoux and Schutzenberger for Hall-Littlewood polynomials, a formula of Sahi and Knop for Jack's symme

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

21 at a point in time modeled as an 8th-degree polynomial, an increase in bilirubin from 1 to 3 mg/dL i

22 Generally, polynomial and allometric models yielded adequate goodne

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

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

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

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

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

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

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

30 The fitting polynomials and constraints have been constructed upon g

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

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

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

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

35 for H(mu) in terms of Lascoux-Leclerc-Thibon polynomials, and combinatorial expressions for the Kostk

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

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

38 A number of significant properties of these polynomials are given (together with outlines of proofs)

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

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

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

42 Fractional polynomials assuming a skewed t distribution were used t

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

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

45 We introduce a polynomial C(mu)[Z; q, t], depending on a set of variabl

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

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

48 We use spectral polynomial chaos expansions to represent statistics of t

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

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

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

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

53 We introduce polynomial collapsing operators for each subnetwork.

54 uidic system to solve a nondeterministically polynomial complete problem (the maximal clique problem)

55 g binding isotherm is described by a binding polynomial consisting of the activities of soluble and i

56 001, and.002, respectively, for t tests with polynomial contrasts).

57 loss was best approximated by a second-order polynomial curve.

58 The basis of the ring of symmetric polynomials defined here is shown to be natural for the

59 A basis set of inhomogeneous symmetric polynomials, denoted by tlambda(z), where z = (z1,....,z

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

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

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

63 with visual acuity, indicating that Zernike polynomials do not fully characterize the surface shape

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

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

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

67 r wide classes of Schrodinger operators with polynomial electric and magnetic fields.

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

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

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

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

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

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

74 Quadratic polynomial equations were developed to best fit the rela

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

76 ng the diffusion equation through a Legendre polynomial expansion.

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

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

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

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

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

82 Polynomial extrapolation of all the data to zero denatur

83 We further introduce a general family of polynomials F(T)[Z; q, S], where T is an arbitrary set o

84 The authors conclude that Zernike polynomials fail to model all the information that influ

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

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

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

88 Polynomial fitting is a conventional baseline correction

89 bove quantitative variables and second-order polynomial fitting.

90 h a mathematical modeling approach utilizing polynomial fitting.

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

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

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

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

95 t limited to dominance but cover any form of polynomial frequency dependence.

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

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 ometry are adequately described by a Zernike polynomial function, although this assumption has not be

102 residues were described with a second degree polynomial function.

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

104 present statistics of the system dynamics as polynomial functions of the model parameters.

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

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

107 Sigmoid or polynomial functions were applied to the curves and maxi

108 for a basis of R* consisting of homogeneous polynomials Gamma[S, C] indexed by pairs of tableaux, wi

109 It shows that chains with polynomial growth (drunkard's walk) do not show cutoffs.

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

111 es were examined in relation to age by using polynomial growth models and data from all available sca

112 t] is none other than the modified Macdonald polynomial H(mu)[Z; q, t].

113 ial interpretation of the modified Macdonald polynomials H(mu).

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

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

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

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

118 L) will have a Hilbert series P(q) that is a polynomial in q with positive integer coefficients.

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

120 ombinatorics are applied to compute the risk polynomial in terms of the fitness landscape.

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

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

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

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

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

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

127 h a mathematical model of elliptic quadratic polynomials in two equal zones: the center and the remai

128 ion of this result to the integral Macdonald polynomials J(mu), a formula for H(mu) in terms of Lasco

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

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

131 Support vector machines (SVMs) with polynomial kernels and constrained optimization models s

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

133 t is closely related to the q-Kostka-Foulkes polynomials Klambdamu(q).

134 of proving the charge interpretation for the polynomials Klambdamu(q).

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

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

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

138 images were leveled with a 12-zone quadratic polynomial mathematical model to minimize background var

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

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

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

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

143 A two-zone, elliptic, quadratic polynomial model can accurately model foveal data.

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

145 A second-order polynomial model satisfactorily fitted the experimental

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

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

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

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

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

151 A second polynomial model was generated and optimised reaction co

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

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

154 which we derived a multivariable fractional polynomial model.

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

156 ed by using linear regression and fractional polynomial modeling.

157 parameters were calculated using fractional polynomial modelling.

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

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

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

161 Using fractional polynomials, modest curvilinear mortality increases (ran

162 Second-degree polynomial multivariable analysis showed a continuous no

163 Spline and polynomial networks form attractive alternatives to MI i

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

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

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

167 results of Nakajima-Yoshioka), and Poincare polynomials of all Nakajima quiver varieties.

168 awski-Dancer and Hausel-Sturmfels), Poincare polynomials of Hilbert schemes of points and twisted Ati

169 Random coefficient modeling considering polynomials of time was used to assess the clinical resp

170 proofs are obtained for formulas of Poincare polynomials of toric hyperkahler varieties (recovering r

171 ble or transformations, including fractional polynomials, of BMI as a continuous variable.

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

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

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

175 3D shape and electrostatic expansions up to polynomial order L=30 on a 2 GB personal computer.

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

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

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

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

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

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

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

183 alue over the central 1 mm by a second-order polynomial (r(2) = 0.38 and P < 0.001 in both cases).

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

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

186 Polynomial regression analyses revealed significant inve

187 Linear and fractional polynomial regression as well as smoothing algorithms we

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

189 Applying multiple polynomial regression enabled us to build phenomenologic

190 lipid, which were analyzed mathematically by polynomial regression for accurate quantitation.

191 We fitted a locally weighted polynomial regression line to daily mortality to estimat

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

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

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

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

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

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

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

199 Polynomial regression was used to model the influence of

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

201 cantly associated with elevation (orthogonal polynomial regression).

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

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

204 count monthly means were also examined with polynomial regression.

205 macaque antibody responses were analyzed by polynomial regression.

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

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

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

209 At last a polynomial retrieval algorithm is introduced.

210 Evaluation of the polynomial reveals an association constant for the forma

211 straightening algorithm, I show that certain polynomials [S, C] closely related to the Gamma[S, C] te

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

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

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

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

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

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

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

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

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

221 ssian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular h

222 The polynomial SVM outperforms previously published predicti

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

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

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

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

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

228 relationships were considered by fractional polynomial terms.

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

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

231 internal equilibrium constants in a binding polynomial that modulates the intrinsic rate of cleavage

232 generally fit with phenomenological binding polynomials that are underdetermined.

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

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

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

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

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

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

239 Unless P=NP, there is no polynomial time algorithm, which when given secondary st

240 Polynomial time algorithms have been proposed for restri

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

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

243 The famous question whether polynomial time equals nondeterministic polynomial time

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

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

246 fast converging methods as well as some new polynomial time methods that we have developed.

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

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

249 ding to a convex optimization problem with a polynomial time solution.

250 possible stars and show that it has a simple polynomial time solution.

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

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

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

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

255 orithm gives optimal performance in expected polynomial time, even when the input graph is significan

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

257 ters, however, could factor integers in only polynomial time, using Shor's quantum factoring algorith

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

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

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

261 could exactly simulate chemical reactions in polynomial time.

262 teleportation scheme for fixed dimension in polynomial time.

263 if it is to guarantee optimality and run in polynomial time.

264 ped in just the last few years, and most are polynomial time.

265 lete problem (the maximal clique problem) in polynomial time.

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

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

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

269 ximation can be found in a relatively short (polynomial) time.

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

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

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

273 We also show that a recently described polynomial-time algorithm for determining whether a cata

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

275 mization problems and develop an approximate polynomial-time algorithm to solve them.

276 We present polynomial-time algorithms for restricted versions of th

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

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

279 e relax the integrality constraint to give a polynomial-time linear programming (LP) heuristic.

280 Our mathematical techniques include new polynomial-time methods for bounding the inversion lengt

281 inversion length of a candidate tree and new polynomial-time methods for estimating genomic distances

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

283 An explicit polynomial-time procedure is described for performing th

284 ng is NP-hard, although a special case has a polynomial-time solution based on the greedy algorithm.

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

286 lementation of the server uses the Alexander polynomial to detect knots.

287 We applied a sigmoid function or third order polynomial to the curves and determined the maximal diff

288 oms were determined by fitting second-degree polynomials to data.

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

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

291 Data were fit with Zernike polynomials up to the seventh order to provide estimates

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

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

294 nalyzed the knots by calculating their Jones polynomials via computer analysis of digital photos of t

295 xponential equations, a general second-order polynomial was derived using a multivariate growth curve

296 Quadratic polynomial was the best fitted mathematical model for th

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

298 over 1000 cross correlations can be fit to a polynomial, which can determine transition times as shor

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

300 be known as the "q, t-Catalan," is in fact a polynomial with positive integer coefficients.

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