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The study was conducted using Design of Experiment (DoE) principles and multivariate analysis approach.
In this study the selection of in vivo predictive in vitro dissolution experimental set-ups using a multivariate analysis approach, in line with the Quality by Design (QbD) principles, is explored.
Multivariate analysis approach analyzes all of the metabolites in the data simultaneously in one analysis.
Piryatinska et al. [75] presented a channel selection method for neonate EEG sleep state classification using a multivariate analysis approach adopting filtering with a complete search strategy for subset channel selection.
To identify the specific plasma metabolites with distributions most affected by subjects' diabetes status, we first employed PCA, an unsupervised multivariate analysis approach, and considered four principal components (PC) dimensions.
By employing a novel multivariate analysis approach, we support the hypothesis of robust AP-1 activation.
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Here, we investigate multivariate analysis approaches that take into consideration the 4D (time and space) covariance structure of the data.
However, some critical differences exist in the outcome of univariate and multivariate analysis approaches, which will be interpreted further in the Discussion.
This interpretation is in agreement with findings from recent fMRI studies on memory for low-level visual features: using multivariate analysis approaches, Harrison and Tong [7], and Serences et al. [8] managed to decode featural attributes of sample stimuli (orientation/orientation + color, respectively) from patterns of activity in V1 during delay periods.
Can multivariate analysis approaches direct the design of effective combination therapies?
Two different multivariate analysis approaches were used to determine and visualize the pattern of similarities within and between cancer and normal spectra.
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