Analysis (PCA) and Linear Discriminant Analysis (LDA) were utilized to classify cell lines using multiple compounds15. Even though analyzing cell lines is an efficient strategy, these results may not be translatable to human or even whole animal studies. Analyzing urine would provide biomarkers that...
Data were explored with principal component analysis (PCA) and classified with linear discriminant analysis (LDA), soft independent modelling by class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). For discrimination and classification, models were developed within each of ...
Here, nuclear magnetic resonance spectroscopy (NMR) in combination with principal components analysis followed by linear discriminant analysis (PCA-LDA) was used for a non-targeted based differentiation of fresh from frozen-thawed fish. To identify the most promising NMR approach(...
Data were explored with principal component analysis (PCA) and classified with linear discriminant analysis (LDA), soft independent modelling by class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). For discrimination and classification, models were developed within each of ...
CT and LGI were estimated using Freesurfer software. Principal Component Analysis and Lineal Discriminant Analysis (LDA) assuming a common diagonal covariance matrix (or Naive-Bayes classifier) was used with cross-validation leave-one-subject-out scheme. Accuracy, sensibility and specificity were ...
CT and LGI were estimated using Freesurfer software. Principal Component Analysis and Lineal Discriminant Analysis (LDA) assuming a common diagonal co-variance matrix (or Naive-Bayes classifier) was used with cross-validation leave-one-subject-out scheme. Accuracy, sensibility and specificity were ...
CT and LGI were estimated using Freesurfer software. Principal Component Analysis and Lineal Discriminant Analysis (LDA) assuming a common diagonal covariance matrix (or Naive-Bayes classifier) was used with cross-validation leave-one-subject-out scheme. Accuracy, sensibility and specificity were ...
The resulting data were analysed using linear discriminant analysis (LDA) and principal component analysis (PCA) to evaluate the E-nose's ability to discriminate between groups. W5S, W1S, W2S, and W2W sensors exhibited the greatest variation in response intensity; in particular, they highlighted...
The resulting data were analysed using linear discriminant analysis (LDA) and principal component analysis (PCA) to evaluate the E-nose's ability to discriminate between groups. W5S, W1S, W2S, and W2W sensors exhibited the greatest variation in response intensity; in particular, they highlighted...
The resulting data were analysed using linear discriminant analysis (LDA) and principal component analysis (PCA) to evaluate the E-nose's ability to discriminate between groups. W5S, W1S, W2S, and W2W sensors exhibited the greatest variation in response intensity; in particular, they highlighted...