ライフサイエンスコーパス: discriminant analysis

1 icornavirus (P < .0001; partial least square discriminant analysis).

2 and chemometric analyses (MANOVA and Linear Discriminant Analysis).

3 entional statistics and partial least square discriminant analysis.

4 e results, further analysed through a linear discriminant analysis.

5 antly associated with GBS carriage by linear discriminant analysis.

6 sured observables following two class linear discriminant analysis.

7 roducers were achieved by applying canonical discriminant analysis.

8 t includes several preprocessing steps and a discriminant analysis.

9 coupled to principal component analysis and discriminant analysis.

10 microarray and protein analysis with linear discriminant analysis.

11 fferentially expressed proteins using binary discriminant analysis.

12 tudied samples was tested by using canonical discriminant analysis.

13 lation with malignant vs benign diagnosis on discriminant analysis.

14 al component analysis, cluster analysis, and discriminant analysis.

15 ochemical parameters using MANOVA and Linear Discriminant Analysis.

16 ion at 18 and 36 mo of age with multivariate discriminant analysis.

17 to model differences among the samples using discriminant analysis.

18 orthogonal projections to latent structures discriminant analysis.

19 nalysis and orthogonal partial least-squares discriminant analysis.

20 component analysis and partial least squares discriminant analysis.

21 ning methods: logistic regression and linear discriminant analysis.

22 hour time series, according to sparse linear discriminant analysis.

23 sing principal component analysis and linear discriminant analysis.

24 ividual indices were built using linear step discriminant analysis.

25 -test, fold changes and partial least square discriminant analysis.

26 r example 0.2 to 13% improvement over linear discriminant analysis.

27 Using Forward Stepwise Discriminant Analysis, 3 statistical models were created

28 cation of origin (Karoo vs. Non-Karoo) using discriminant analysis allowed 95% and 90% correct classi

29 Linear discriminant analysis allowed some grouping of the varie

30 Linear discriminant analysis allowed the differentiation of fru

31 cipal component analysis, followed by linear discriminant analysis, allowed bacterial discrimination.

32 We sought to determine whether discriminant analysis allows prognostication in patients

33 Partial least squares discriminant analysis also allowed prediction of the deg

34 Partial least squares-discriminant analysis also showed that these markers wer

35 Discriminant analysis and a receiver operating character

36 Partial Least Squares regression, Discriminant Analysis and Artificial Neural Networks wer

37 It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separati

38 inant techniques, i.e. partial least squares discriminant analysis and k-nearest neighbours algorithm

39 Linear discriminant analysis and multilayer perceptron artifici

40 Partial least squares-discriminant analysis and orthogonal bidirectional PLS-D

41 ed by using orthogonal partial least-squares-discriminant analysis and paired t tests adjusted for mu

42 alysis, linear discriminant analysis, binary discriminant analysis and random forest.

43 on rank sum test, t-score, cat-score, binary discriminant analysis and random forests provided largel

44 Discriminant analysis and SVM were used to classify new

45 ned use of principal component analysis with discriminant analysis and ultra-high-performance liquid

46 lysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Discriminant Anal

47 forward feature selection by means of linear discriminant analysis, and lesion classification by usin

48 significance testing, partial least squares discriminant analysis, and receiver operating characteri

49 al component analysis, partial least squares discriminant analysis, and support vector machines discr

50 were analyzed by using partial least-squares discriminant analysis, and the results were validated wi

51 s based on principal components analysis and discriminant analysis applied to the volatile profile of

52 ralized linear model approach and the linear discriminant analysis approach are able to increase imag

53 d in 143 samples for AR classification using discriminant analysis (area under the receiver operating

54 A simplified discriminant analysis based on 3 common clinical variabl

55 On the other hand, linear discriminant analysis based on 8 selected absorbance val

56 discrimination models were created by linear discriminant analysis based on principal component analy

57 The discriminant analysis based on several anthocyanins, org

58 Simplified discriminant analysis based on unsupervised clustering h

59 The discriminant analysis, based on the use of one input-cla

60 Nine variables survived a linear discriminant analysis between HP, SZ, and BDP.

61 hine, diagonal discriminant analysis, linear discriminant analysis, binary discriminant analysis and

62 Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares).

63 The study confirms that discriminant analysis can be successful in distinguishin

64 odourprintings were obtained by a canonical discriminant analysis carried out with the aim of relati

65 A principal components based linear discriminant analysis classification model was developed

66 Using linear discriminant analysis, classification rules for authenti

67 Furthermore, linear discriminant analysis classified 100% of the samples cor

68 e image features were combined with a linear discriminant analysis classifier and evaluated both on t

69 tor variables were derived to train a linear discriminant analysis classifier by using a leave-one-ou

70 Partial least square discriminant analysis clearly separated IC patients from

71 ication models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection

72 Results demonstrated that Discriminant Analysis (DA) and Correlated Component Regr

73 ch as principal component analysis (PCA) and discriminant analysis (DA) have become an integral part

74 Partial least-squares (PLS)-discriminant analysis (DA) was employed to evaluate the

75 using principal component analysis (PCA) and discriminant analysis (DA) which revealed that analytes

76 n harvesting years was studied by performing discriminant analysis (DA), k nearest neighbours (kappa-

77 ty of multivariate analysis methods, such as discriminant analysis (DA), was used to achieve cherry c

78 uding principal component analysis (PCA) and discriminant analysis (DA).

79 Multivariate partial least squares discriminant analysis demonstrated similar bacterial pro

80 ics, we have developed partial least-squares-discriminant analysis derived decision algorithms that o

81 Discriminant analysis distinguished severe asthma from C

82 eparation between the two groups, and linear discriminant analysis effect size (LEfSe) method reveale

83 nt of CDI were identified by means of linear discriminant analysis effect size analysis and then furt

84 d algorithm, expectation-maximization sparse discriminant analysis (EM-SDA), produces a sparse LDA mo

85 analysis, principal component analysis, and discriminant analysis enabled the accurate characterizat

86 Fisher Discriminant Analysis enables multivariate classificatio

87 The combination of DRIFTS and discriminant analysis enables simple, rapid, cheap and a

88 Linear discriminant analysis established a positive correlation

89 For the principal component analysis and discriminant analysis, excellent percentages of correct

90 paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data.

91 Recently, Witten proposed a Poisson linear discriminant analysis for RNA-Seq data.

92 and sunflower) were distinguished by linear discriminant analysis from their element content.

93 Three of 5 clusters identified by means of discriminant analysis had improved SNOT-22 outcomes with

94 ach pair-wise lesion type comparison, linear discriminant analysis helped identify the most discrimin

95 We determined that several iterations of the discriminant analysis improved the classification of sub

96 From discriminant analysis in 103 patients with full clinical

97 e data trends and clusters, and then, linear discriminant analysis in order to detect the set of vola

98 Discriminant analysis in the MCI cases assigned statisti

99 mework, an iterative, voxelwise, regularized discriminant analysis is combined with a receiver operat

100 nsform micro-Raman spectroscopy coupled with Discriminant Analysis is here presented.

101 Discriminant analysis is used to reduce the dimension of

102 ng n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based class

103 Linear discriminant analysis (LDA) and partial least squares di

104 The authentication issue was faced by Linear Discriminant Analysis (LDA) and Soft Independent Modelli

105 nd computational approaches including linear discriminant analysis (LDA) and sparse canonical correla

106 amples was most successful when using linear discriminant analysis (LDA) and taking into account the

107 A linear discriminant analysis (LDA) based on concentrations of 1

108 xtracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their

109 In addition, Linear discriminant analysis (LDA) effect size (LEfSe) method r

110 rincipal component analysis (PCA) and linear discriminant analysis (LDA) for the differentiation of p

111 dentifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the corr

112 Although Linear Discriminant Analysis (LDA) is commonly used for classif

113 A digital filter derived from linear discriminant analysis (LDA) is developed for recovering

114 rincipal component analysis (PCA) and linear discriminant analysis (LDA) is proposed for the authenti

115 ed as original variables to construct linear discriminant analysis (LDA) models.

116 racted from response data and used in Linear Discriminant Analysis (LDA) plots, including a full 3-di

117 Linear discriminant analysis (LDA) successfully recognizes the

118 We applied linear discriminant analysis (LDA) to simulation output and ide

119 ential forward selection coupled with linear discriminant analysis (LDA) was the best strategy to sel

120 A machine-learning algorithm called linear discriminant analysis (LDA) was trained by using the lar

121 s Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the res

122 rincipal component analysis (PCA) and linear discriminant analysis (LDA) were performed.

123 ysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA), and discriminant unfolded p

124 Principal component analysis (PCA), linear discriminant analysis (LDA), k-nearest neighbors (kNN),

125 attern recognition techniques such as linear discriminant analysis (LDA), partial least square discri

126 rinciple component analysis (PCA) and linear discriminant analysis (LDA).

127 erarchical cluster analysis (HCA) and linear discriminant analysis (LDA).

128 rincipal component analysis (PCA) and linear discriminant analysis (LDA).

129 y classification models, specifically linear discriminant analysis (LDA).

130 rincipal component analysis (PCA) and linear discriminant analysis (LDA).

131 omponent analysis (PCA) followed by a linear discriminant analysis (LDA).

132 th multivariate statistical analysis: linear discriminant analysis (LDA).

133 sing a statistical analysis method of linear discriminant analysis (LDA).

134 component analysis (PCA) followed by linear discriminant analysis (LDA).

135 rincipal component analysis (PCA) and linear discriminant analysis (LDA).

136 modelled by support vector machine, diagonal discriminant analysis, linear discriminant analysis, bin

137 A discriminant analysis method was employed in the classif

138 Fisher's linear discriminant analysis method was employed to classify aw

139 ed training set, (18)F-FDG PET with advanced discriminant analysis methods is able to accurately dist

140 e classical population-based Anderson linear discriminant analysis minimax sampling ratio derived fro

141 A Linear Discriminant Analysis model based on the abundance of 12

142 constructed orthogonal partial least squares discriminant analysis models (septic shock vs ICU contro

143 Several Orthogonal Partial Least Squares-Discriminant Analysis models were generated from the acq

144 by means of orthogonal partial least-squares discriminant analysis models.

145 urements obtained from partial least-squares discriminant analysis models.

146 ed diagnostic groups (multiblock barycentric discriminant analysis [MUBADA]) was used.

147 applying the tested Partial Least Squares - Discriminant Analysis multiclass model (85 fillets) to t

148 lysis) and supervised (Partial Least Squares Discriminant Analysis) multiparametric statistical metho

149 -Orthogonal Projections to Latent Structures-Discriminant Analysis (O2PLS-DA) model was found for dis

150 Discriminant analysis of isotope and minerals values all

151 A stepwise quadratic discriminant analysis of mRNA measures identified a line

152 The Discriminant Analysis of MultiAspect CYtometry (DAMACY)

153 ysis was validated using the assumption-free Discriminant Analysis of Principal Components (DAPC) met

154 iated genetic clusters were revealed through discriminant analysis of principal components (DAPC), bu

155 Structure, discriminant analysis of principal components and princi

156 Furthermore, data analyses--using discriminant analysis of principal components and random

157 also reflected in both network analyses and discriminant analysis of principal components.

158 to decide which method should be used in the discriminant analysis of RNA-Seq data.

159 res by using PLS and ANN methods, led to the discriminant analysis of sugar contents.

160 Using multivariate linear discriminant analysis of the EEG, we identified multiple

161 Partial least-squares discriminant analysis of the urine NMR data found unique

162 Linear discriminant analysis on the physicochemical parameters

163 orthogonal projections to latent structures-discriminant analysis (OPLS-DA) model was found with R(2

164 orthogonal projections to latent structures discriminant analysis (OPLS-DA) using GC/MS data had exc

165 Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully

166 Afterwards, orthogonal partial least square discriminant analysis (OPLS-DA) was favorably used to di

167 Orthogonal partial least square discriminant analysis (OPLS-DA) was used to determine wh

168 analysed by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) which revealed a clear d

169 Using orthogonal partial least-squares discriminant analysis (OPLS-DA), a multivariate model wa

170 Using orthogonal partial last-squares discriminant analysis (OPLS-DA), multivariate models wer

171 LS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA).

172 s (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA).

173 is, PCA, and Orthogonal Partial Least Square Discriminant Analysis, OPLS-DA).

174 ned with principal component analysis-linear discriminant analysis or variable selection techniques e

175 jects were classified into 3 subgroups using discriminant analysis, or disease status with a binary a

176 the validation subjects to subgroups: linear discriminant analysis, or the best identified discrimina

177 in combination with principal component and discriminant analysis, partial least-squares, and princi

178 via principal component analysis and linear discriminant analysis (PCA and LDA, respectively), inclu

179 nd multivariate principal component analysis-discriminant analysis (PCA-DA) statistics applied to the

180 ncipal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Square

181 by using principal component analysis-linear discriminant analysis (PCA-LDA) on 3D rendered MSI volum

182 ncipal component analysis followed by linear discriminant analysis (PCA-LDA) was used for the multiva

183 sing principal component analysis and linear discriminant analysis (PCA-LDA), it was demonstrated tha

184 analysis (PARAFAC) and Partial least squares Discriminant Analysis (PLS DA) were used for characteriz

185 to be classified using partial least-squares discriminant analysis (PLS-DA) according to octane ratin

186 In addition, partial least square discriminant analysis (PLS-DA) achieved an effective cla

187 Furthermore, the partial least square discriminant analysis (PLS-DA) achieved an effective cla

188 ur algorithm, based on partial least squares-discriminant analysis (PLS-DA) and cross-validation by b

189 lanobis distance (MD), partial least squares discriminant analysis (PLS-DA) and k nearest neighbours

190 Partial least squares discriminant analysis (PLS-DA) and orthogonal projection

191 opy in combination with Partial Least Square Discriminant Analysis (PLS-DA) and Partial Least Square

192 py, and analysed using partial least squares discriminant analysis (PLS-DA) and partial least squares

193 earest neighbors (kNN), partial least square-discriminant analysis (PLS-DA) and support vector machin

194 (MIR) spectroscopy and partial least squares discriminant analysis (PLS-DA) as a means to discriminat

195 Among the samples, partial linear square discriminant analysis (PLS-DA) classified 50.2% of the s

196 rediction errors using partial least squares discriminant analysis (PLS-DA) discrimination models for

197 The partial least-squares discriminant analysis (PLS-DA) model built to discrimina

198 We report partial least-squares discriminant analysis (PLS-DA) models of single cell Ram

199 ent analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to disc

200 Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and

201 Four different partial least squares discriminant analysis (PLS-DA) models were fitted to the

202 bolomics combined with partial least squares-discriminant analysis (PLS-DA) multivariate analysis rev

203 Partial least squares-linear discriminant analysis (PLS-DA) provided a 70% success ra

204 fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be th

205 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA) showed discrimination of

206 Using partial least-squares discriminant analysis (PLS-DA) to compare results betwee

207 s were developed using partial least squares discriminant analysis (PLS-DA) to distinguish between ex

208 Partial least squares discriminant analysis (PLS-DA) was used to determine spe

209 Partial least squares discriminant analysis (PLS-DA) was used to develop a hie

210 Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR d

211 ert system (FuRES) and partial least-squares-discriminant analysis (PLS-DA) were evaluated in paralle

212 ant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) were performed as classif

213 pal component analysis (PCA) followed by PLS-discriminant analysis (PLS-DA) were used to classify fru

214 Partial least-squared discriminant analysis (PLS-DA) with double leave-one-pat

215 Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS we

216 ectra was subjected to partial least squares-discriminant analysis (PLS-DA), a multivariate statistic

217 Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant

218 ponent analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial l

219 Using partial least squares discriminant analysis (PLS-DA), it was possible to diffe

220 iminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-N

221 ecognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modelin

222 rest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine c

223 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA), the purees may be alloca

224 QTOF-MS) followed by a partial linear square-discriminant analysis (PLS-DA), were used to compare the

225 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

226 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

227 copy combined with partial least squares for discriminant analysis (PLS-DA).

228 hieved using projection to latent structures discriminant analysis (PLS-DA).

229 ed classification with Partial Least Squares Discriminant Analysis (PLS-DA).

230 s combined, the partial least squares-linear discriminant analysis (PLS-LDA) model resulted in 75% an

231 tric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied

232 frared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach.

233 was obtained using the partial least squares discriminant analysis (PLSDA) algorithm.

234 sification models with partial least-squares discriminant analysis (PLSDA) and obtaining average stoc

235 Here, we presented a partial least squares discriminant analysis (PLSDA) method based on the NIR sp

236 least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN)

237 ent analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were performed on the MS

238 The output of the linear discriminant analysis provided a qEEV-based response ind

239 turbations [orthogonal partial least-squares discriminant analysis Q(2)(Y) of 0.728] in the fecal lip

240 mponent analysis (PCA) followed by quadratic discriminant analysis (QDA) and K-means cluster analysis

241 , was 100% as determined by use of quadratic discriminant analysis (QDA).

242 rate classification by partial least squares discriminant analysis, recursive-support vector machine,

243 VOI-based discriminant analysis resulted in an 88.8% accuracy in p

244 Linear Discriminant Analysis revealed significant clustering ba

245 Linear discriminant analysis reveals shape features that specif

246 riables selected by means of stepwise linear discriminant analysis (S-LDA).

247 nstruction, and sparse-partial least squares-discriminant analysis (s-PLS-DA) allow data size reducti

248 nearest shrunken centroids (NSCs) and sparse discriminant analysis (SDA) with k-nearest neighbors for

249 tificial neural network, expert opinion, and discriminant analysis separated the data set into no-blo

250 Discriminant analysis showed delta(13)C and delta(15)N d

251 The cluster analysis and linear discriminant analysis showed that samples were grouped i

252 Stepwise Linear Discriminant Analysis (SLDA) showed that a reduced numbe

253 component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to develop a disc

254 Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA) for simultaneous classif

255 We propose a sparse version of Quadratic Discriminant Analysis (SQDA) to explicitly consider the

256 Canonical discriminant analysis suggested that the gut microbiomes

257 Support vector machines-discriminant analysis (SVM-DA) was used for differentiat

258 neighbors (KNN), and support vector machines discriminant analysis (SVMDA).

259 t compares favorably to a regularized linear discriminant analysis, SVMs in a one against all multipl

260 Further, a VOI derived by discriminant analysis that maximally separated diagnosti

261 Applying factor and/or discriminant analysis, the cactus pad samples were clear

262 noids were considered as variables in linear discriminant analysis to attempt geographical classifica

263 ent salinity sources and then employs linear discriminant analysis to classify samples from different

264 lected by gas chromatography associated with discriminant analysis to differentiate milk and whey, as

265 The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extr

266 of the femur, tibia, and patella and linear discriminant analysis to identify vectors that best clas

267 The application of linear discriminant analysis to our overall results demonstrate

268 uares Discriminant Analysis) and LDA (Linear Discriminant Analysis), to implement a traceability syst

269 o 5 clusters based on a previously described discriminant analysis using total Sino-Nasal Outcome Tes

270 Models were built using linear discriminant analysis via the misclassification penalize

271 A posteriori-corrected discriminant analysis was able to correctly classify 95%

272 The Linear Discriminant Analysis was able to discriminate different

273 A discriminant analysis was applied taking as dependent va

274 Linear discriminant analysis was applied to discriminate betwee

275 The linear discriminant analysis was conducted in order to classify

276 Discriminant analysis was done both volume of interest b

277 A partial least squares discriminant analysis was employed to identify the sprea

278 Discriminant analysis was extended to the evaluation of

279 Partial least-squares discriminant analysis was performed on the NMR data to c

280 A linear discriminant analysis was performed to compare the miner

281 Orthogonal partial least squares-discriminant analysis was performed to select features c

282 Discriminant analysis was successfully discriminated the

283 Orthogonal partial least squares discriminant analysis was used to build models separatin

284 Principal component fed linear discriminant analysis was used to develop a classificati

285 A linear discriminant analysis was used to establish discriminant

286 f criteria for AMR and partial least squares discriminant analysis was used to identify associated ch

287 ysis (e.g., orthogonal partial least squares discriminant analysis), we were able to distinguish the

288 Using sequencing-based miRNA profiling and discriminant analysis, we identified the tumor-specific

289 ectral Raman data with partial least-squares discriminant analysis, we show that a surprisingly large

290 omponent analysis, and partial least squares discriminant analysis, we were able to segregate abiotic

291 ntensity variations, principal component and discriminant analysis were performed to discriminate the

292 transform infrared spectroscopy (DRIFTS) and discriminant analysis were used for the geographical dif

293 ipal component analysis, factor analysis and discriminant analysis were used for the statistical eval

294 Principal component analysis and discriminant analysis were utilised to create a model fo

295 parameters, as also confirmed in the linear discriminant analysis, where these parameters were not s

296 ical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the

297 component analysis and partial least squares discriminant analysis, which indicates that endogenous m

298 richment analysis, and partial least-squares discriminant analysis with LASSO feature selection.

299 The best results were obtained using Discriminant Analysis, with 95% correct re-classificatio

300 , both individually and combined, via linear discriminant analysis, with receiver operating character