Sentence examples for principal normalization from inspiring English sources

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0.784 A_UG R_Outside University 0.659 Variable principal normalization Table 4 Perception component loadings   Dimension 1 P_R_Relevant 0.613 P_R_Only for Future Scientists −0.592 P_R_Learn about Field 0.656 P_R_Skills 0.786 P_R_Lonely −0.648 Table 5 Mentoring component loadings   Dimension 1 M_Work with Faculty 0.905 M_Ask Faculty 0.831 M_Join Program 0.853.

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Upon rotation to principal components and normalization to unit variance, the resulting spatial distribution of words displays distinct geometric features associated with corresponding word meanings, i.e. it constitutes a semantic map (Figure 1).

After microarray data normalization, Principal Component Analysis was performed on the average gene expression profiles of the experimental groups.

After normalization, principal component analysis (PCA) was performed to visualize differences between groups and t-test statistics were performed to test for differential expression.

In the Appendix, we derive approximations for the normalized principal eigenvalue ⟨ν⟩∞ and the normalized average connectivity ⟨ν⟩ o of the neutral network, where in both cases the normalization is obtained by dividing by the network coordination number.

To minimize the neural network architecture, normalization and principal component analysis are done on the input data before it is applied to the neural network.

After data normalization, the principal components were considered significant if they contributed more than 5% for the total variance.

Upon microarray hybridization and data normalization, unsupervised principal component analysis (PCA) was performed to visualize differences between liver profiles of control and treated animals.

In this paper we investigate this bias for two common data preparation steps: normalization and principal component analysis for dimension reduction of the covariate space (PCA).

The assessment of coverage for every housekeeping gene in all our selected ENCODE data sets was carried out exactly as described above for HERVd, including normalization and principal component analysis (The featureCount output for all housekeeping genes can be found in Additional file 3).

In addition, or as an alternative to the above constraint, one option is to constrain the principal curve, or alternatively the normalization function, such that the normalized data will be on the same scale as one of the sources, which is then referred to as the target source.

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