ライフサイエンスコーパス: discriminant

1 neal posterior elevation, showed the highest discriminant ability (AUC: 0.951).

2 Linear discriminant analyses (LDA) were performed in order to c

3 ory overviews were performed and then linear discriminant analyses (LDA) were used for classification

4 A series of discriminant analyses demonstrated that the strength of

5 Discriminant analyses revealed that combined assessment

6 Multivariate partial least squares discriminant analyses were applied to determine whether

7 Discriminant analyses were performed between high I.Q. (

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

25 Linear discriminant analysis (LDA) successfully recognizes the

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

107 Linear discriminant analysis allowed the differentiation of fru

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

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

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

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

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

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

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

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

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

117 Discriminant analysis and SVM were used to classify new

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

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

120 A simplified discriminant analysis based on 3 common clinical variabl

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

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

123 The discriminant analysis based on several anthocyanins, org

124 Simplified discriminant analysis based on unsupervised clustering h

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

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

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

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

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

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

131 Partial least square discriminant analysis clearly separated IC patients from

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

133 Multivariate partial least squares discriminant analysis demonstrated similar bacterial pro

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

135 Discriminant analysis distinguished severe asthma from C

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

137 Fisher Discriminant Analysis enables multivariate classificatio

138 Linear discriminant analysis established a positive correlation

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

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

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

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

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

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

145 From discriminant analysis in 103 patients with full clinical

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

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

148 Discriminant analysis is used to reduce the dimension of

149 A discriminant analysis method was employed in the classif

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

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

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

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

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

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

156 The Discriminant Analysis of MultiAspect CYtometry (DAMACY)

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

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

159 Structure, discriminant analysis of principal components and princi

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

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

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

163 Linear discriminant analysis on the physicochemical parameters

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

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

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

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

168 Linear Discriminant Analysis revealed significant clustering ba

169 Linear discriminant analysis reveals shape features that specif

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

171 Canonical discriminant analysis suggested that the gut microbiomes

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

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

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

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

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

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

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

179 Linear discriminant analysis was applied to discriminate betwee

180 The linear discriminant analysis was conducted in order to classify

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

182 Discriminant analysis was extended to the evaluation of

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

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

185 Discriminant analysis was successfully discriminated the

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

217 entional statistics and partial least square discriminant analysis.

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

219 sured observables following two class linear discriminant analysis.

220 antly associated with GBS carriage by linear discriminant analysis.

221 roducers were achieved by applying canonical discriminant analysis.

222 t includes several preprocessing steps and a discriminant analysis.

223 coupled to principal component analysis and discriminant analysis.

224 microarray and protein analysis with linear discriminant analysis.

225 fferentially expressed proteins using binary discriminant analysis.

226 tudied samples was tested by using canonical discriminant analysis.

227 lation with malignant vs benign diagnosis on discriminant analysis.

228 ividual indices were built using linear step discriminant analysis.

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

230 All quantitative analyses (eg, discriminant and convergent validity correlations, known

231 Samples were submitted to discriminant and descriptive sensory analysis.

232 Combining individually discriminant and synergic genes can improve the predicti

233 reliability, construct validity (convergent, discriminant, and known group), predictive validity, and

234 and gene expression profiles, with the major discriminant being expression of the adaptation-linked g

235 lly expressed genomic features as learning a discriminant boundary in a multi-dimensional space of ba

236 The discriminant capability for the risk of death was tested

237 tion (PseAAC) and introducing the covariance discriminant (CD) algorithm, in which a bias-adjustment

238 Recent modifications enhance its discriminant characteristics and its ability to accurate

239 Fertilizer administration provides a discriminant classification of the chicory cultivars acc

240 Classification was performed using a linear discriminant classifier and validated on an untouched co

241 ve compounds while Chardonnay wines had more discriminant compounds.

242 Extensive comparisons of Discriminant-Cut with 13 existing methods were carried o

243 An effective algorithm, Discriminant-Cut, has been developed to solve an instant

244 The most discriminant differences between lincRNAs and mRNAs invo

245 controlled trials of adults with severe AH (discriminant function >/=32 and/or hepatic encephalopath

246 with alcoholic hepatitis (modified Maddrey's discriminant function >32), nine with alcohol-related ci

247 ssification of the Frederick Mikelberg (FSM) discriminant function (hazard ratio [HR] 2.51, 95% confi

248 e used to perform a stepwise cross-validated discriminant function analysis (DFA).

249 omponent analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial leas

250 iking atrophy patterns) and the results of a discriminant function analysis that incorporated clinica

251 nalysis to identify objective call types and discriminant function analysis to assess context specifi

252 tive data from 384 Chinese children and used discriminant function analysis to determine the best ana

253 Discriminant function analysis was used to estimate suit

254 , using in vivo intracellular recordings and discriminant function analysis, we found that the respon

255 The discriminant function containing maximum ectasia indices

256 ltivariate model validation showed very good discriminant function in predicting kidney discard (AUC

257 profiles of each layer and output values of discriminant functions based on individual indices.

258 Multimeric discriminant functions combined with individual indices

259 ng informative genes, including individually discriminant genes and synergic genes, from expression d

260 each comparable accuracy to the individually discriminant genes using the same number of genes.

261 s more difficult than selecting individually discriminant genes.

262 Integrated discriminant improvement analysis showed that the TYM-MC

263 uses a machine-learning approach to extract discriminant information from a broad array of features

264 s within New York City provides richer, more discriminant information on influenza incidence, particu

265 We have examined the potential of discriminant inorganic constituents (trace-, ultra-trace

266 rted in citrus essential oils, from which 38 discriminant markers were defined.

267 novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-

268 Discriminant metabolites were considered if within the t

269 A linear discriminant model used to distinguish brain parenchyma

270 Furthermore, a discriminant model was created for tumor types (i.e., gl

271 Stepwise discriminant modeling showed correct classification of 1

272 Three PLS-discriminant models with low prediction errors were cons

273 iscrimination properties as well as distance-discriminant neurons are revealed in the avian auditory

274 methods tested for discovery of MS features discriminant of dietary intake in these urinary metabolo

275 we show that nuclear shape is a quantifiable discriminant of mechanical properties in the perinuclear

276 ER status and grade are better discriminants of survival than the presence of small-vol

277 1 after correction for multiple testing) and discriminant [pcorr (1) > 0.3, VIP > 1.5] analyses showe

278 Cs were finally selected for their excellent discriminant performance in identifying disease-free pat

279 group that is clearly separated in a linear discriminant plane from up(101) and hdp(2) (cardiomyopat

280 repeated cross-validation that optimizes its discriminant power.

281 The most discriminant Raman modes were identified based on VIP (v

282 than those in the acquisition group (linear discriminant score, 3.97; P = .04).

283 stigation of the impact of dispersion on the discriminant score.

284 ly of compounds seemed to be responsible for discriminant sensory terms in Champagne base wines.

285 ting various invasiveness phenotypes contain discriminant spectral features, which are useful informa

286 roccan oils to evaluate the feasibility of a discriminant Sr signature on the two geographical produc

287 rlapping zones; consequently, two supervised discriminant techniques, i.e. partial least squares disc

288 aimed at identifying position-specific, most-discriminant thresholds in sliding windows along the seq

289 We also design a novel subcategory discriminant transform (SDT) algorithm to further enhanc

290 esolution with alternating least-squares and discriminant unfolded partial least-squares (D-UPLS).

291 d by linear discriminant analysis (LDA), and discriminant unfolded partial least-squares (DU-PLS).

292 Pain Observation Tool demonstrated excellent discriminant validity as evidenced by a highly statistic

293 These findings demonstrate the discriminant validity between similar prosocial construc

294 Discriminant validity was described using paired t tests

295 Discriminant validity was supported by weak correlations

296 rnal consistency reliability, convergent and discriminant validity were found to be good for the DAS

297 There was good convergent and discriminant validity, with significant and positive cor

298 the Vancouver risk calculator yielded a high discriminant value, which supports the use of a risk cal

299 The most discriminant variables identified as responsible for geo

300 The main design discriminant was the holding temperature; increased temp