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 The discriminant ability of the pipeline was evaluated with

3 in PD (PD > 4 mm) and suppuration were good discriminants amongst PIM/PIMP.

4 A case-control study was performed using discriminant analyses to validate an approach using Mtb-

5 Linear discriminant analyses were performed to develop predicti

6 , mild MPP group and control group in linear discriminant analyses.

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

8 ndent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra

9 Discriminant Analysis (DA) led to similar (conventional/

10 Discriminant Analysis (DA) was successful for botanical

11 density, were processed using multivariate, discriminant analysis (DA), principal component analysis

12 the year, season and production region using discriminant analysis (DA).

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

14 The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is

15 e developed a novel method based on Fisher's Discriminant Analysis (FDA) to identify gene expression

16 spectral data; genetic algorithm with linear discriminant analysis (GA-LDA) achieved the best diagnos

17 results obtained by Genetic Algorithm Linear Discriminant Analysis (GA-LDA) were the most satisfactor

18 ined using the genetic algorithm with linear discriminant analysis (GA-LDA).

19 etic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA).

20 hine classification and Linear and Quadratic Discriminant Analysis (LDA and QDA, respectively).

21 and class modelling methods by using linear discriminant analysis (LDA) and soft independent model c

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

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

24 s an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning met

25 Moreover, using Linear Discriminant Analysis (LDA) coupled with effect size mea

26 In this study, Linear Discriminant Analysis (LDA) is applied to investigate th

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

28 mong the multivariate analysis tools, linear discriminant analysis (LDA) of fruit multi elemental fin

29 rincipal component analysis (PCA) and linear discriminant analysis (LDA) showed better results consid

30 of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea see

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

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

33 Raman spectroscopy with linear discriminant analysis (LDA) was used to identify changes

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

35 tern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by s

36 type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cyto

37 rincipal component analysis (PCA) and linear discriminant analysis (LDA), an identification model pro

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

39 d with discriminant analysis, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Anal

40 In this paper, Linear Discriminant Analysis (LDA), Quadratic Discriminant Anal

41 , principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SV

42 rincipal component analysis (PCA) and linear discriminant analysis (LDA), were used to differentiate

43 ochastic neighbor embedding (tSNE) or linear discriminant analysis (LDA).

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

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

46 of ecological character states using linear discriminant analysis (LDA).

47 -weighted multiblock partial least squares - discriminant analysis (MB-PLS1-DA) models were compared

48 lling using Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) of metabolomic data read

49 An orthogonal partial least squares discriminant analysis (OPLS-DA) score plot revealed good

50 Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) showed clear group disti

51 ) followed by orthogonal partial last square-discriminant analysis (OPLS-DA) to develop classifiers a

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

53 s (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to characte

54 ysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and hierarchical cluste

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

56 d Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA).

57 as well as Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).

58 ysis (PLS-DA) and Principal Component-Linear Discriminant Analysis (PC-LDA) classification models wer

59 Principal components linear discriminant analysis (PC-LDA) of acquired RS-spectra co

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

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

62 ns using principal component analysis linear discriminant analysis (PCA-LDA).

63 and principal component analysis with linear discriminant analysis (PCA-LDA).

64 region of 4000-5000 cm(-1), piecewise linear discriminant analysis (PLDA) is used to classify spectra

65 sion 7.2 software) and partial least squares-discriminant analysis (PLS-DA) (Matlab R2017b) were used

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

67 On the basis of the partial least-squares-discriminant analysis (PLS-DA) and ANOVA-simultaneous co

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

69 ierarchical clustering, Partial-least square discriminant analysis (PLS-DA) and Ingenuity pathway ana

70 odels were developed by Partial Least Square-Discriminant Analysis (PLS-DA) and internally validated

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

72 Partial-Least Squares-Discriminant Analysis (PLS-DA) and Principal Component-L

73 Partial least squares discriminant analysis (PLS-DA) and soft independent mode

74 Partial least squares discriminant analysis (PLS-DA) and soft independent mode

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

76 s (PCA) and supervised partial least squares-discriminant analysis (PLS-DA) from liquid chromatograph

77 Partial Least Squares-Discriminant Analysis (PLS-DA) identified variables to d

78 Partial least-squares discriminant analysis (PLS-DA) indicated that both metho

79 Partial least square-discriminant analysis (PLS-DA) indicated that seeds from

80 Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine lear

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

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

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

84 recognition analysis, partial-least-squares discriminant analysis (PLS-DA) of images, to develop a r

85 Partial Least Squares-Discriminant Analysis (PLS-DA) of metabolomes of individ

86 Partial least squares discriminant analysis (PLS-DA) showed clear discriminati

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

88 troscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) to discriminate the origi

89 overall structure, and partial least square discriminant analysis (PLS-DA) was carried out for the a

90 Next, partial least squares-discriminant analysis (PLS-DA) was developed to detect f

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

92 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investig

93 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were evaluated.

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

95 ts were obtained for partial least squares - discriminant analysis (PLS-DA), allowing a good discrimi

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

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

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

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

100 ogistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explor

101 ass analogy (SIMCA) and partial least square discriminant analysis (PLS-DA).

102 ic algorithm (GA); and partial least squares discriminant analysis (PLS-DA).

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

104 fied and submitted to a Partial-Least Square Discriminant Analysis (PLS-DA).

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

106 nt analysis (PCA), and partial least squares-discriminant analysis (PLS-DA).

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

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

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

110 inear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines

111 inear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines

112 ML algorithm, an implementation of quadratic discriminant analysis (QDA) written in MATLAB.

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

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

115 ve projections algorithm coupled with linear discriminant analysis (SPA-LDA) classified correctly all

116 regression (sSVR) for calibration, and sPLS-discriminant analysis (sPLS-DA) and support vector class

117 system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector mach

118 was achieved by super partial least-squares discriminant analysis (sPLS-DA), support vector machine

119 PERMANOVA and sparse partial least squares discriminant analysis (sPLSDA) demonstrated that subject

120 Extreme gradient boosting discriminant analysis (XGBDA) was examined among other m

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

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

123 Furthermore, linear discriminant analysis allowed achieving sensitivities gr

124 e plots and orthogonal partial least squares discriminant analysis also showed significant difference

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

126 ction of food frauds were analyzed employing discriminant analysis and a one-class classifier.

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

128 Multivariate classification comprises discriminant analysis and class-modeling techniques wher

129 cipal component analysis coupled with linear discriminant analysis and classification models demonstr

130 ombination of principal component and linear discriminant analysis and least absolute shrinkage and s

131 ted by linear Support Vector Machine, Linear Discriminant Analysis and Naive Bayesian classifiers acr

132 cipal component analysis coupled with linear discriminant analysis and predictive classifiers were ap

133 metric techniques like partial least squares discriminant analysis and variable identification (PLS-D

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

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

136 Including all studied regions and using discriminant analysis between PSP and PD, 100% sensitivi

137 oss-validation was employed to test a Linear Discriminant Analysis classifier based upon the RT-qPCR-

138 A discriminant analysis correctly classified 96.7% of the

139 thin palmitate and total carotenoid content, discriminant analysis correctly classified all samples a

140 Yet, discriminant analysis did not adequately distinguish the

141 Linear discriminant analysis distinguished facial change determ

142 uation logistic regression models and linear discriminant analysis effect size (LEfSe) algorithm to i

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

144 Fisher Discriminant Analysis enables multivariate classificatio

145 ror, 2.03-12.48%) were acquired by nonlinear discriminant analysis for CRC, IBD and NTC, regardless o

146 lysis using orthogonal partial least squares discriminant analysis for metabolomics.

147 SOM-RPM can be further analyzed using linear discriminant analysis for the validation and characteriz

148 Partial least squares discriminant analysis identified a signature of 54 basel

149 rometry performed, with partial least square discriminant analysis integrating clinical, microbiome,

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

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

152 d anatomy ontology terms, employing Fisher's Discriminant Analysis methods to identify previously unk

153 e features were used as input to a quadratic discriminant analysis ML classifier, which was trained,

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

155 on of the groups in the partial least square-discriminant analysis model was based, demonstrated the

156 Partial Least Squares-Discriminant Analysis modelling of fused PCA scores of t

157 ak volume ratios were used to develop linear discriminant analysis models able to distinguish olive l

158 r data exploration and partial least squares discriminant analysis models for the differences between

159 Three partial least square-discriminant analysis models were developed in order to

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

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

162 a(13)C values, apatite-collagen spacing, and discriminant analysis of delta(13)C(coll), delta(13)C(ca

163 The Discriminant Analysis of MultiAspect CYtometry (DAMACY)

164 Discriminant analysis of principal components (DAPC) rev

165 = 0.65), Bayesian analysis in STRUCTURE and discriminant analysis of principal components (DAPC) sho

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

167 Partial least squares-discriminant analysis of the MS/MS(ALL) lipidomic datase

168 Linear discriminant analysis on the physicochemical parameters

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

170 omparison of locomotor kinematics and linear discriminant analysis reveal a surprisingly similar patt

171 Partial least square-discriminant analysis revealed metabolite profiles that

172 Discriminant analysis revealed that for each grape culti

173 Partial least squares-discriminant analysis showed that the 30 most important

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

175 orthogonal) projections to latent structures-discriminant analysis to characterise and differentiate

176 itudinal abundance of metabolites; 4) linear discriminant analysis to evaluate robustness in a second

177 e developed a novel method based on Fisher's Discriminant Analysis to identify gene expression weight

178 e conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discr

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

180 Moreover, discriminant analysis using ASD subdomains distinguished

181 dministration and the multivariate canonical discriminant analysis was able to distinguish between tr

182 Partial least squares discriminant analysis was applied to the spectral datase

183 The linear discriminant analysis was conducted in order to classify

184 Then partial least-squares-discriminant analysis was conducted to investigate the b

185 an exploratory assessment relying on Linear Discriminant Analysis was performed.

186 Discriminant analysis was successfully discriminated the

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

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

189 IR in combination with Partial Least Squares-Discriminant Analysis we were able to discriminate infec

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

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

192 The aging type could be defined by linear discriminant analysis with an accuracy of 95%.

193 differentiation models were built by linear discriminant analysis with the percentage of correct cla

194 The LDA (linear discriminant analysis) model showed that the FAMEs conte

195 nalysis (principal component analysis-linear discriminant analysis), this method was successfully app

196 e fingerprints (Q(2) of 0.784 for supervised discriminant analysis).

197 ponent analysis (PCA), partial least squares-discriminant analysis, analysis of variance, and random

198 ysis (PARAFAC), PARAFAC supervised by linear discriminant analysis, and discriminant unfolded partial

199 Partial least squared (PLS)-discriminant analysis, and PLS-regression models were as

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

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

202 ombination of dimension reduction and linear discriminant analysis, CARS (AUC = 0.93) and TPEF (AUC =

203 Linear Discriminant Analysis, coupled with Successive Projectio

204 ed, via thermophoretic enrichment and linear discriminant analysis, for cancer detection and classifi

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

206 ent analysis, PCA, and partial least squares discriminant analysis, PLS-DA) were applied to deconvolu

207 feature selection algorithms associated with discriminant analysis, such as Linear Discriminant Analy

208 In discriminant analysis, these baseline factors identified

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

210 Using linear discriminant analysis, XRF-based multi-elements with and

211 was the best predictor of reentry in linear discriminant analysis.

212 erve as keystone taxa as revealed our linear discriminant analysis.

213 samples were correctly classified by linear discriminant analysis.

214 ear support vector machine and kernel Fisher discriminant analysis.

215 cluding the t-test and partial least squares discriminant analysis.

216 ogistic regression and partial least squares discriminant analysis.

217 e statistical technique Partial Least Square-Discriminant Analysis.

218 antly associated with GBS carriage by linear discriminant analysis.

219 tatistical technique of partial least square discriminant analysis.

220 ividual indices were built using linear step discriminant analysis.

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

222 ns to latent structures (OPLS) and OPLS with discriminant analysis].

223 stems using a 10-fold cross-validated linear-discriminant-analysis using Fourier-transform infrared s

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

225 PLS-DA-VID, SOM-EFS proved more effectively discriminant and improved the median five-fold cross-val

226 scolarctobacterium were also selected in the discriminant and linear regression analyses, and could b

227 Combining individually discriminant and synergic genes can improve the predicti

228 support vector machines, traditional linear discriminant, and k-nearest neighbor statistical analyse

229 These interplays with discriminant bacterial taxa in HT and NT subjects highli

230 d probing depth (PD) were confirmed reliable discriminants between peri-implant health and disease, w

231 and 25 bacterial taxa previously defined as discriminant biomarkers among groups.

232 ivity and specificity assessment showed high discriminant capability in the real serum sample.

233 l also be analysed as its reliability or its discriminant capacity.

234 Tenfold cross-validations using a linear discriminant classifier with predefined feature signatur

235 t flavonoids characterizing PS were the most discriminant compounds during the in vitro digestion.

236 lection (SOM-EFS) and PLS-DA-VID to identify discriminant compounds in 17 blue cheese varieties.

237 Aged beans exhibited more discriminant compounds than fresh beans regardless of te

238 Three-common discriminant compounds were exclusive to each blend and

239 ation (PLS-DA-VID) may not identify the most discriminant compounds.

240 nd Libournais vineyard highlighting the most discriminant constituents.

241 tive cancer procedures, and determine if the discriminant cut-off of hospital volume may influence po

242 The most discriminant differences between lincRNAs and mRNAs invo

243 By sampling along the discriminant dimension, and back-projecting into the ima

244 roteins (1,425 cm(-1)) were identified to be discriminant features.

245 holipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in disco

246 f BMO-MRW and pRNFL parameters with a linear discriminant function (LDF) could further enhance glauco

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

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

249 Discriminant function analysis was used to examine how a

250 rowear variables were combined into a single discriminant function analysis, the cast data and origin

251 .9 to 19.4 +/- 3.7 (P = 0.002) and Maddrey's discriminant function from 74.8 +/- 22.8 to 57.4 +/- 31

252 The discriminant functions classify 100% of the wines, with

253 Multimeric discriminant functions combined with individual indices

254 Two discriminant functions were generated and allowed a sati

255 Results: Three discriminant functions were identified and defined as ma

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

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

258 y a previously established model using seven discriminant genes.

259 Integrated discriminant improvement analysis showed that the TYM-MC

260 ercetin, and quercetin 3-O-glucuronide to be discriminant in the detection of the oxidative status of

261 al regions and spectral components that were discriminant in the different cognitive tasks.

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

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

264 The manganese content was a discriminant marker of Liebana PDO and El Bierzo, that c

265 Other discriminant metabolites included: 1-monostearin, xylono

266 Discriminant metabolites were considered if within the t

267 It is shown that the discriminant method is only partially appropriate for so

268 A partial least squares-discriminant model is first trained from 53 oil samples

269 (taken 4-14 h after wake-up time), a linear discriminant model with wrapper-based feature selection

270 Discriminant modelling of the spectral data successfully

271 The discriminant models based on E-nose dataset enable a 100

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

273 ay provide brain age estimates that are most discriminant of individuals with pathologies.

274 s, furofurans and phenolic acids as the most discriminant phenolics.

275 tra were evaluated with partial least square-discriminant (PLS-DA) and PLS to identify fat/oil origin

276 twenty-one volatile markers with the highest discriminant power for varietal differentiation.

277 ates robust representations that have strong discriminant power in distinguishing kinase substrates f

278 repeated cross-validation that optimizes its discriminant power.

279 negative predictive values and accuracy and discriminant property of the FOBS were determined and co

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

281 ature selection algorithms (namely, Fisher's discriminant ratio, Kruskal-Wallis' analysis, and Relief

282 apability of discrimination, emphasizing the discriminant role of some elements.

283 A discriminant score based on both indicators (D = -0.42 -

284 with an exhaustive search approach to find a discriminant set of image features, which were validated

285 s the raw time series obviating the need for discriminant statistic while accommodating multiple time

286 ng and investigates potential differences in discriminant statistics between the given empirical samp

287 The choice and estimation of the discriminant statistics can be challenging across short

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

289 ervised by linear discriminant analysis, and discriminant unfolded partial least-squares.

290 ons through hybridisation, but morphological discriminants used for species identification are unable

291 al component score and the SF-6D showed good discriminant validity as judged by the analysis of varia

292 These findings demonstrate the discriminant validity between similar prosocial construc

293 Convergent and discriminant validity of the PROMIS measures was support

294 The DLQI had better convergent and discriminant validity than the SF-12.

295 re for convergent validity assessment, while discriminant validity was established through the abilit

296 1H-MRS showed adequate discriminant validity, but limited reliability and poor

297 ations with AD severity assessments and poor discriminant validity.

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

299 n area and species, and to identify the most discriminant VOCs.

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