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To obtain an overview of the transcriptional landscape, we looked at the data using principal components analysis (PCA), a dimensional reduction technique which identifies "principal components" or major trends in gene expression in the overall data (Figure 3B).
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The method of multiple scales is applied to analytically identify principal and combination instability boundaries as closed-form expressions.
Principal Component Analysis (PCA) was applied to identify principal components (PCs) corresponding to the structures of reactants and solvent of each set.
With the genome-wide determination of TSSs, we identified principal elements of gene expression, such as promoters and SD sequences, regulatory RNA elements, and unannotated transcripts.
We identify principal metrics, provide a theoretical model and perform the assessment evaluation using a high performance simulator that is based on a parallel and distributed architecture.
Exploratory factor analysis was used with the first data set to identify principal factors that explained the majority of problem areas.
This paper analysis and discusses on recent achievements and future trends on research for Enterprise Integration and Networking solutions, identifying principal challenges for this research area.
Principal component analysis (PCA) was used to identify principal drivers regulating spatial and temporal distribution patterns of heavy metals in the river sediments.
This study used SAS 9.4 software to identify principal components and the correlation between the principal components and the original variables.
Principal component analysis (PCA) was used to identify principal components (PC) which account for the majority of the variation within the dataset.
We identified principal investigators (PIs) for projects funded through Improving Undergraduate STEM Education IUSEE) and Widening Implementation and Demonstration of Evidence-Based Reforms (WIDER) programs.
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