ライフサイエンスコーパス: least squares

1 analysis, and discriminant unfolded partial least-squares.

2 gative matrix factorization with alternating least-squares algorithm (NMF-ALS) to solve spectral over

3 ncipal component analysis (PCA), and partial least squares analysis (PLS) revealed that the overall a

4 stic categories were not used in the partial least squares analysis but were helpful for interpreting

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

6 Partial least squares analysis permitted to correctly classify w

7 We employed multivariate sparse partial least squares analysis to detect parsimonious associatio

8 adult brain gene expression data and partial least squares analysis to find the weighted gene express

9 Using partial least squares analysis, we identified latent variables r

10 Using multivariate partial-least-squares analysis, we observed a significant patter

11 an hierarchical approach that uses two-stage least squares and applied it to an ATAC-seq (assay for t

12 Both sparse partial least squares and kernel canonical correlation analysis

13 tivariate techniques (such as sparse partial least squares and kernel canonical correlation analysis)

14 tivariate techniques, such as sparse partial least squares and kernel canonical correlation analysis,

15 We estimated multivariate ordinary least squares and logistic regression models controlling

16 Orthogonal partial least squares and multiple linear regression analyses id

17 lel Factor Analysis (PARAFAC), N-way partial least squares and partial least squares discrimination a

18 nterval of D that outperformed the classical least-squares approach in terms of coverage probability

19 ria for model fitting, such as the method of least squares, are modified by imposing a penalty for ea

20 ands is suppressed by correction model using least squares background correction.

21 polysomnography-confirmed iRBD using partial least squares between brain deformation and 27 clinical

22 .2-74.5), respectively; the placebo-adjusted least-squares between-group difference in mean change fr

23 to 0.31), respectively; the placebo-adjusted least-squares between-group difference in mean change fr

24 and praliciguat groups, the placebo-adjusted least-squares between-group difference in mean change in

25 ctorial finite element method (VFEM) and the least squares boundary residual (LSBR) method.

26 ied correctly all samples, while the partial least squares coupled with SPA for interval selection (i

27 rm multivariate curve resolution alternating least squares decomposition of the spectral dataset to d

28 Non-negative least-squares deconvolution enabled the reading of an un

29 An orthogonal partial least squares discriminant analysis (OPLS-DA) score plot

30 ponent analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were appli

31 component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and hiera

32 sis (ICA, PCA) as well as Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).

33 Partial least squares discriminant analysis (PLS-DA) and soft in

34 Partial least squares discriminant analysis (PLS-DA) and soft in

35 Partial least squares discriminant analysis (PLS-DA) showed clea

36 (ATR-MIR) spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) to discrimi

37 incipal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employ

38 incipal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were evalua

39 SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA).

40 riate supervised classification with Partial Least Squares Discriminant Analysis (PLS-DA).

41 PERMANOVA and sparse partial least squares discriminant analysis (sPLSDA) demonstrate

42 analysis score plots and orthogonal partial least squares discriminant analysis also showed signific

43 , linear chemometric techniques like partial least squares discriminant analysis and variable identif

44 ti-variate analysis using orthogonal partial least squares discriminant analysis for metabolomics.

45 Partial least squares discriminant analysis identified a signatu

46 is was used for data exploration and partial least squares discriminant analysis models for the diffe

47 Partial least squares discriminant analysis was applied to the s

48 incipal component analysis, PCA, and partial least squares discriminant analysis, PLS-DA) were applie

49 is methods, including the t-test and partial least squares discriminant analysis.

50 amined using logistic regression and partial least squares discriminant analysis.

51 Partial least-squares discriminant analysis (PLS-DA) indicated t

52 Partial Least-Squares Discriminant Analysis (PLS-DA) is a popula

53 The partial least-squares discriminant analysis (PLS-DA) model built

54 d with pattern recognition analysis, partial-least-squares discriminant analysis (PLS-DA) of images,

55 incipal-component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were perfor

56 Classification was achieved by super partial least-squares discriminant analysis (sPLS-DA), support v

57 mportance measurements obtained from partial least-squares discriminant analysis models.

58 " set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metaboli

59 NMR) profiles were analyzed by using partial least-squares discriminant analysis, and the results wer

60 weighted and non-weighted multiblock partial least squares - discriminant analysis (MB-PLS1-DA) model

61 Best results were obtained for partial least squares - discriminant analysis (PLS-DA), allowing

62 tivariate modelling using Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) of metabol

63 PCA) (OPUS Version 7.2 software) and partial least squares-discriminant analysis (PLS-DA) (Matlab R20

64 Partial-Least Squares-Discriminant Analysis (PLS-DA) and Princip

65 ponent analysis (PCA) and supervised partial least squares-discriminant analysis (PLS-DA) from liquid

66 Partial Least Squares-Discriminant Analysis (PLS-DA) identified

67 Partial Least Squares-Discriminant Analysis (PLS-DA) of metabolo

68 Next, partial least squares-discriminant analysis (PLS-DA) was develop

69 ts, adjusted logistic regression and partial least squares-discriminant analysis (PLS-DA), while grou

70 ncipal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA).

71 Partial Least Squares-Discriminant Analysis modelling of fused P

72 Partial least squares-discriminant analysis of the MS/MS(ALL) li

73 Partial least squares-discriminant analysis showed that the 30 m

74 s, with ATR-FTIR in combination with Partial Least Squares-Discriminant Analysis we were able to disc

75 Principal component analysis (PCA), partial least squares-discriminant analysis, analysis of varianc

76 A partial least squares-discriminant model is first trained from 5

77 On the basis of the partial least-squares-discriminant analysis (PLS-DA) and ANOVA-s

78 uilding expert system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and suppo

79 Then partial least-squares-discriminant analysis was conducted to inv

80 h two groups were obtained using the partial least squares discriminate analysis of 9 lipid metabolit

81 AC), N-way partial least squares and partial least squares discrimination and regression (NPLS-DA, PL

82 plied the Williamson-York bivariate weighted least squares estimation to preserve the errors in both

83 ion of water and fat with echo asymmetry and least-squares estimation (IDEAL) technique.

84 set as a function of Deltaomega by a linear least-squares fit.

85 ent in processing times compared to standard least squares fitting techniques.

86 alysis by implementing a non-negative linear least-squares fitting algorithm in conjunction with a CD

87 In addition, a nonlinear least-squares fitting procedure is utilized to predict t

88 ting ensures optimal parameter estimation in least-squares fitting, with exact parameter standard err

89 essing of infected population through linear least-squares fitting.

90 We used multiple regression to estimate least squares geometric means of phthalate biomarker con

91 ent, and using circular dichroism and matrix least-squares Henderson-Hasselbalch global fitting, unra

92 sed an Image Downsampling Expedited Adaptive Least-squares (IDEAL) fitting algorithm that quantifies

93 Compared to univariate analysis, partial least squares improves typical sensing performance param

94 ordinate descent based iteratively reweighed least squares (IRLS) algorithm has been proposed.

95 ission data were projected using an ordinary least squares linear regression model.

96 Ordinary least squares linear regression of SAP mean deviation (M

97 mated by the SD of the residuals of ordinary least squares linear regressions of SAP mean deviation (

98 did a cross-sectional analysis using general least-squares linear models to assess group differences

99 d progression were calculated using ordinary least-squares linear regression of standard automated pe

100 all fibroblast strains combined, the partial least squares-linear discriminant analysis (PLS-LDA) mod

101 y associated with genetic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA).

102 (intent-to-treat population [n = 145]), the least squares (LS) mean changes (standard error [SE]) in

103 orticosteroid-naive participants (p = 0.088; least squares [LS] mean 0.042 [95% CI -0.007, 0.091]), b

104 he multivariate curve resolution-alternating least squares (MCR-ALS) algorithm for multiset analysis

105 ng multivariate curve resolution-alternating least squares (MCR-ALS) and support vector machine (SVM)

106 Multivariate curve resolution-alternating least squares (MCR-ALS) assisted with electrochemical te

107 ed Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the evaluation, resolution a

108 multivariate curve resolution by alternating least-squares (MCR-ALS) enhanced with signal shape const

109 Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when deal

110 Multivariate curve resolution-alternating least-squares (MCR-ALS) was applied to LC-DAD, LC-FLD, a

111 Multivariate curve resolution-alternating least-squares (MCR-ALS) was applied to the UVRR spectra,

112 st multivariate curve resolution alternating least-squares (MCR-ALS) was employed for simultaneous re

113 d, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while

114 e. multivariate curve resolution alternating least squares, MCR-ALS, followed by principal component

115 Least squares mean (SE) change from predose to 30 min po

116 Least squares mean (standard error [SE]) changes from ba

117 s with higher total protein intake (adjusted least squares mean +/- SE: <0.8 g/kg/d, 829 +/- 17 ng/ml

118 patients in the conventional therapy group (least squares mean +1.9 [SE 0.1] with burosumab vs +0.8

119 umber of drinks per week between the groups (least squares mean 10.4 drinks per week [SD 16.5] in the

120 ovements in the primary outcome of ppFEV(1) (least squares mean [LSM] treatment difference of 10.0 pe

121 t week 24 from baseline in SGRQ total score (least squares mean [SE] change from baseline -15.6 (1.0)

122 oncentrations decreased in nasal secretions (least squares mean area under the curve from 0 to 16 wee

123 Over the primary evaluation period, the least squares mean average total combined score in the 3

124 The greatest change from baseline (least squares mean change -17.3 [95% confidence interval

125 Least squares mean change from baseline to week 6 in AIM

126 ions in serum phosphate level from baseline (least squares mean change: tenapanor =0.47-1.98 mg/dl; p

127 t and -3.5 days (-4.0 to -3.0) with placebo (least squares mean difference -0.8 days, 95% CI -1.46 to

128 eek 12 (-32.9% resmetirom vs -10.4% placebo; least squares mean difference -22.5%, 95% CI -32.9 to -1

129 ith pitavastatin and 20.9% with pravastatin (least squares mean difference -9.8%, 95% CI -13.8 to -5.

130 cantly reduced mean 24-hour IOP vs. vehicle (least squares mean difference [95% confidence interval]:

131 The least squares mean difference compared with placebo in a

132 At 24 weeks, least squares mean difference in NPS of dupilumab treatm

133 At Week 4 (after the first treatment), the least squares mean difference in the AE-QoL and DLQI sco

134 in the placebo group (4.35), with a relative least squares mean difference of -16.9% (95% CI, -24.0%

135 ndpoint of sweat chloride concentration, the least squares mean difference versus placebo was -20.8 m

136 isits up to and including the week 24 visit, least squares mean difference was -1.09 units (95% CI -1

137 nificance after adjustment for multiplicity (least squares mean difference, -0.2 [95% CI=-0.5, 0.0] f

138 between switchers and nonswitchers (adjusted least squares mean difference, -1.36 letters; 95% CI, -2

139 oup and 11.8 +/- 15.8% in the placebo group (least squares mean difference: -4.9%, 95% CI: -16.9, 7.1

140 group; and 7.3 to 4.4 in the placebo group (least squares mean differences [95% CI] vs placebo were

141 e symptoms (reducing MADRS total score); the least squares mean differences were -2.5 (95% CI=-4.6, -

142 Least squares mean differences were estimated using a mi

143 Least squares mean diurnal IOP (+/- standard error) at m

144 pidem-CR had a significant treatment effect (least squares mean estimate=-0.26, SE=0.12, 95% CI=-0.50

145 observed on the Scale for Suicide Ideation (least squares mean estimate=-0.56, SE=0.83, 95% CI=-2.19

146 Changes in least squares mean from baseline to week 97 favoured enz

147 Serum phosphorus least squares mean increase from baseline to week 40 of

148 hacher Rickets Severity Score decreased by a least squares mean of -1.7 (SE 0.1; p<0.0001) from basel

149 At week 24, the least squares mean percent weight change from baseline w

150 so indicated significant improvement, with a least squares mean score of +2.3 (SE 0.1) at week 40 and

151 fer between any bimagrumab dose and placebo (least squares mean treatment difference for bimagrumab 1

152 24 weeks of treatment was 21.39+/-2.93L/min (Least squares mean+/-standard error).

153 days to a greater than GDMT alone (adjusted least squares mean: -4.0 vs. -0.9 mm Hg; p = 0.006), a c

154 The least-squares mean (+/-SE) change in the HAM-D score fro

155 ion with the Bolus or Divided dose increased least-squares mean (95% CI) milk and infant intakes of r

156 all five atogepant groups showed significant least-squares mean (SE) change from baseline in mean mon

157 eks of blinded treatment, improvement in the least-squares mean (SE) HAM-D-24 scores were similar bet

158 the primary outcome, in the probiotics+BBR (least-squares mean [95% CI], -1.04[-1.19, -0.89]%) and B

159 s over 12 weeks were greater versus placebo (least-squares mean [LSM] change -0.6 [SE 0.3]) with quar

160 mean decitabine systemic exposure (geometric least-squares mean [LSM]) of oral/IV 5-day area under cu

161 -65.0 to -33.1; P<0.001); the between-group least-squares mean absolute difference in the LDL choles

162 At week 16, the differences in the least-squares mean change from baseline in the LDL chole

163 both pembrolizumab plus pemetrexed-platinum (least-squares mean change: 1.0 point [95% CI -1.3 to 3.2

164 with pembrolizumab plus pemetrexed-platinum (least-squares mean change: 1.3 points [95% CI -1.2 to 3.

165 Adjusted least-squares mean changes in total UPDRS score in the a

166 nd 318.3 m, and 295.8 m and 311.4 m, and the least-squares mean changes were 5.0 m, 8.7 m, and 10.5 m

167 .0, and 59.0 and 67.1, respectively, and the least-squares mean changes were 5.5, 6.4, and 6.9, respe

168 The least-squares mean difference (combined REGN-COV2 dose g

169 educed mean left ventricular wall thickness (least-squares mean difference +/- SEM: -0.9+/-0.4 mm, P=

170 not improve 6MWD versus placebo at 26 weeks (least-squares mean difference 21 m; 95% CI -9 to 52).

171 The least-squares mean difference between the 15-mg/d verici

172 The least-squares mean difference between the groups in seru

173 and -10.3+/-1.3 points in the placebo group (least-squares mean difference in change, -7.0 points; 95

174 The least-squares mean difference in scores between the 15-m

175 The least-squares mean difference in UPDRS motor score chang

176 9% in the placebo group, for a between-group least-squares mean difference of -49.0 percentage points

177 The least-squares mean difference versus placebo with respec

178 (95% CI 0-150; p=0.04) greater improvement (least-squares mean difference) in prebronchodilator FEV1

179 e was -48% with AK002 and -22% with placebo (least-squares mean difference, -26 percentage points; 95

180 -17.2 points and -9.7 points, respectively (least-squares mean difference, -7.5 points; 95% confiden

181 p, as compared with 9% in the placebo group (least-squares mean difference, -98 percentage points; 95

182 and from 18.7 to 17.1 in the control group (least-squares mean difference, 1.7 points; 95% CI, 0.0 t

183 9 at 4 months in the active-treatment group (least-squares mean difference, 9.8 points; 95% confidenc

184 29 (31%) of 94 patients were responders; the least-squares mean estimate of the proportion of respond

185 Least-squares mean HIV-1 RNA at 7 days after dose decrea

186 Least-squares mean monthly HAE attack rate for lanadelum

187 At day 180, the least-squares mean reductions in LDL cholesterol levels

188 A changes from baseline (ETDRS letters) were least squares means of +1.1 (95% confidence interval [CI

189 odel, changes from baseline are presented as least squares means with 95% CIs.

190 excreted 41% less alpha-CEHC (all values are least-squares means +/- SEMs: 0.6 +/- 0.1 compared with

191 Differences in least-squares means versus vehicle for the primary end p

192 Adjusted least-squares means were calculated to compare dietary i

193 haracteristics and used one-sample two stage least squares Mendelian randomization (2SLS MR) to show

194 Thus, the weighted least squares method is shown to be a safe linear regres

195 ulatory networks using a two-stage penalized least squares method.

196 The non-negative linear least-squares method yields the best results for deconvo

197 Least squares minimization with the definition LA(I) = 0

198 The obtained partial least squares model successfully shows the distribution

199 Using partial least squares modelling, we observed a negative effect o

200 Partial least squares models were built using data fusion at low

201 method boils down to the linear non-negative least squares (NNLS) problem, whereas proportions of the

202 based method to estimate SACE using ordinary least squares (OLS) regression can be biased if the trea

203 of a biased inference compared with ordinary least squares (OLS) regression.

204 The training is done using ordinary least squares (OLS), and we leverage distributional resu

205 end-based analysis was performed by ordinary least-squares (OLS) linear regression of global RNFL thi

206 We used segmented ordinary least-squares (OLS) regression using Newey-West standard

207 up scores were modeled by orthogonal partial least squares (OPLS) analysis with good fit and predicti

208 Orthogonal partial least squares (OPLS) associated a butyrate ester of pino

209 An easily updatable nonlinear least squares optimization is used to reconcile the inco

210 ological traits were evaluated using partial least-squares path modeling, and a consensus model was d

211 Notably, using partial least-squares-path modeling we found that WM insult with

212 ariate model was generated using the partial least squares (PLS) algorithm, and linear correlations w

213 incipal component analysis (PCA) and partial least squares (PLS) analysis have been investigated.

214 Seed partial least squares (PLS) analysis showed that this SES-based

215 combined with chemometric methods - partial least squares (PLS) and artificial neural networks (ANN)

216 n prediction were obtained using the partial least squares (PLS) calibration achieving limits of dete

217 A Partial Least Squares (PLS) model was constructed, giving a pred

218 and regression vector statistics of partial least squares (PLS) modeling to deduce directional relat

219 rial strains, a fluorescent dye, and partial least squares (PLS) modeling was developed to assess the

220 Partial least squares (PLS) models successfully predicted bone w

221 Partial least squares (PLS) models were built individually with

222 Partial least squares (PLS) regression was used to develop analy

223 modelling of CH(4) was conducted by partial least squares (PLS) regression, fitting calibration mode

224 a robust calibration model, such as partial least squares (PLS) regression, is a laborious task beca

225 individual treatment response into a partial least squares (PLS) regression.

226 'Tempranillo' grape clones and with Partial Least Squares (PLS) regressions to predict its contents

227 specific chemometrics tools, such as partial least squares (PLS), interval-PLS, synergy interval-PLS

228 onical correlation analysis (CCA), penalized least squares (PLS), various approaches have been propos

229 m infrared (FTIR) spectrometer using partial least-squares (PLS) regression models.

230 incipal component analysis (PCA) and partial least-squares (PLS) regression was used to determine the

231 based on multivariate calibration by partial least-squares (PLS), the proposed strategy takes advanta

232 Partial least squares predictive models were developed and incor

233 Second, an L2-regularized least-squares problem is solved to infer values of the c

234 Partial Least Squares provided good correlations between the bio

235 Preprocessed data were analysed with partial least squares regression (PLS) to model the wine sensory

236 ng multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (D

237 infrared region (NIR) combined with partial least squares regression (PLS), which is a clean and fas

238 ory variables were analyzed applying partial least squares regression (PLS).

239 el and nu regression (SVM-P(NU)) and partial least squares regression (PLS).

240 n and dimensional analyses including partial least squares regression (PLS-R) and sparse partial leas

241 egression coefficients (RC) from the partial least squares regression (PLSR) model based on the raw d

242 e parameters were investigated and a partial least squares regression (PLSR) model was developed for

243 ssociated with embryo malformations, partial least squares regression (PLSR) modelling was applied.

244 Partial Least Squares Regression (PLSR) models applied to NIR-HS

245 Partial least squares regression (PLSR) models based on full spe

246 o predict Ca content in INF samples, partial least squares regression (PLSR) models that developed ba

247 We used Partial Least Squares Regression (PLSR) on structural MRI data f

248 Partial least squares regression (PLSR) revealed correlations be

249 Multivariate analysis with partial least squares regression (PLSR) was performed to correla

250 Finally, partial least squares regression (PLSR) was used to estimate the

251 Simple multilinear methods, such as partial least squares regression (PLSR), are effective at interr

252 the ASTA color values of paprika by partial least squares regression (PLSR).

253 1 month at a time until the r(2) in weighted least squares regression (r(2)(WLS)) was maximized for t

254 quares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in

255 analytical curves were estimated by weighted least squares regression (WLS), confirming heteroscedast

256 Partial least squares regression analysis suggested that BG and

257 Further, partial least squares regression analysis was used to estimate c

258 Partial least squares regression and multiple linear regression

259 Also, partial least squares regression and principal component regress

260 d the MPCs by using two methods, namely, the least squares regression and the response surface method

261 of Orthogonal Signal Correction and Partial Least Squares regression are proposed for prediction of

262 A Sparse Partial Least Squares regression model was able to explain the c

263 We further developed a partial least squares regression model, which highlighted mitoch

264 Partial least squares regression models are developed to capture

265 Ordinary least squares regression models examined associations be

266 tion was a significant slope of the ordinary least squares regression of a simulated patient's mean d

267 ation, we performed phylogenetic generalized least squares regression on cecal length and body mass w

268 Further, we used partial least squares regression to compare ZFL endogenous metab

269 calibration models were built using Partial Least Squares regression to determine dry matter (DM), s

270 We use data-driven partial least squares regression to identify two separable compo

271 content in a dough system, based on partial least squares regression using colour images.

272 up to 33.1% in the treatment group (ordinary least squares regression with robust standard errors (d.

273 py in combination with chemometrics (Partial Least Squares Regression) can predict the hardness devel

274 tes, we developed trait models using partial least squares regression, and mapped 26 foliar traits in

275 Additional chemometric modelling, a partial least squares regression, has correctly classified sampl

276 In a multivariate ordinary least squares regression, the analysis estimated the log

277 This double-waveform partial-least-squares regression (DW-PLSR) paradigm permits reli

278 sequent full voltammetric scan using partial-least-squares regression (PLSR).

279 ter-free chemometrics methods, super partial least-squares regression (sPLSR) and super support vecto

280 We performed sparse partial least-squares regression analyses followed by ordinary l

281 In a 2-stage least-squares regression analysis, each genetically inst

282 tral models were constructed using a partial least-squares regression approach.

283 xternal validation (i.e. by applying partial least-squares regression coefficients on a dataset disti

284 l series, and by applying generalized linear least-squares regression modelling to components of the

285 res regression analyses followed by ordinary least-squares regression to assess the multipollutant as

286 We used ordinary least-squares regression to assess variation in log(BMI)

287 signatures (identified using sparse partial least-squares regression) using causal mediation BKMR mo

288 AC components were used to develop a partial least-squares regression-based model (r(2) = 0.53; Nash-

289 area p24 using both elastic net and partial least-squares regression.

290 logical (RPD > 3.1) parameters using partial least-squares regression.

291 a using simulated annealing and refined by a least-squares Rietveld refinement procedure.

292 rug interaction profiles and the Regularized Least Squares (RLS) classifier.

293 roach (Sequential and Orthogonalized-Partial Least Squares - SO-PLS) has been used, in order to explo

294 ltigroup analysis using multivariate partial least-squares structural equation models, to generate an

295 use the multivariate analysis method partial least squares that combines multiple features of the sur

296 crete model and use the method of non-linear least squares to estimate the age-specific annual rate o

297 Partial Least Squares was used to identify brain regions in whic

298 A multivariate data-driven approach (partial least squares) was used to identify latent components li

299 ures), and we estimated them using nonlinear least squares with standard, free software.

300 h exact parameter standard errors for linear least-squares with known data variance.