ライフサイエンスコーパス: least-squares analysis

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1 ometrics (i.e., demonstrated using classical least-squares analysis).

2 results were correlated by means of partial least squares analysis.

3 and evaluating their accuracy with a partial least-squares analysis.

4 d rate constants were obtained by nonlinear, least-squares analysis.

5 Orthogonal partial least-squares analysis allowed the recovery of a predict

6 First, combining partial least squares analysis and genetic algorithm, we fitted

7 as been incorporated into a global nonlinear least-squares analysis approach, based upon the Marquard

8 ) and 90 degree angle (alpha)) obtained from least squares analysis are r(C=C) = 1.346(4) A, r(C-C)(r

9 rate constants determined through nonlinear least-squares analysis are also in agreement with protei

10 gle(alpha)) obtained from the combined ED/MW least-squares analysis are r(C-H)(av) = 1.093(6) A, r(C(

11 Partial least squares analysis based on 325 measured parameters

12 An inverse least-squares analysis coupled to a Michaelis-Menten pro

13 calibration method, two-dimensional partial least-squares analysis, for calibrating single-particle

14 near-infrared (NIR) spectrometry and partial least-squares analysis has been developed for the noninv

15 ing curvature, parameters estimated from the least-squares analysis have varying degrees of uncertain

16 -component lifetime models used in nonlinear least-squares analysis in which each lifetime component

17 a multivariate curve resolution-alternating least-squares analysis method has been used to character

18 Using two-dimensional least-squares analysis methods, the Raman spectra collec

19 A multilevel, within-group, sparse partial least squares analysis of covariation of microbial, infl

20 es and estimate B using a nonlinear weighted least squares analysis of nighttime MLS ClO data.

21 ture ionic liquid (RTIL) followed by partial least squares analysis of the data.

22 Simultaneous nonlinear least squares analysis of the spectra obtained at the tw

23 ent observables through a weighted nonlinear least-squares analysis of a constrained model.

24 Our workflow consists of nonlinear least-squares analysis of steady-state spectroscopic mea

25 eric forms of an analyte followed by partial least-squares analysis of the data.

26 Least-squares analysis of the FTIR data of native BNC yi

27 Global non-linear least-squares analysis of the full kinetic time-courses

28 Least-squares analysis of the spectrum indicates an effe

29 The method involves simultaneous (global) least-squares analysis of titrations with Ca(2+), with M

30 bination of simulation and global non-linear least-squares analysis provides support for a binding mo

31 Partial Least Squares analysis revealed one latent variable that

32 Partial least-squares analysis revealed age-related increases in

33 ses and describe novel methods for nonlinear least-squares analysis that overcome these problems.

34 etic algorithm and couples them to a partial least squares analysis to predict cellular function, and

35 The model was refined by least-squares analysis to a nominal resolution of 2.1 A

36 Simultaneous nonlinear least-squares analysis using "n-step" sequential mechani

37 Nonlinear least squares analysis was performed to optimize fitting

38 mately 465-630 nm) were modeled using linear least squares analysis with individual chromophore spect

39 (1.2 +/- 0.4) x 104 M-1 s-1 using nonlinear least-squares analysis, yielding an equilibrium binding

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