Sentence examples for using a pca based from inspiring English sources

To investigate the substructure in the transcriptomes of late larvae and adult worms, we ordered all transcriptomes using a PCA based approach implemented in the software BLIND [ 29].

It has been shown previously that qualitative results can be obtained and here we are showing that those results can be converted into quantitative ones, using a PCA based reconstruction algorithm.

For a likely development scenario, we assessed the correlative structure among projected indicator responses and, using a PCA-based analysis of outcomes, identified both patterns of potential redundancies and ecological processes linking indicators and dominant processes influencing VCs.

As shown in Elhaik et al. [ 21], the most suitable number of components was verified using a PCA-based analysis.

We used this class of fingerprint for developing a PCA based model.

Therefore, we decided to use PCA, based on the argument that PCA enabled us to incorporate the highest number of experts in our results.

Evaluating the efficiency of detecting PCa using a model based on family history, age, and genetic variation, Zheng et al. suggested comparable efficiency in detecting PCa when compared to the predictive power of PSA level cutoff of 4.1 ng/mL (see Table 1 for an overview of all SNPs cited in this paper) [ 32].

We used a principle component analysis (PCA) based on a correlation matrix to contrast the most important climatic factors between the species niches in Past 3.01 [ 80].

For this analysis, a wealth index was created using principal components analysis (PCA), based on the ownership of the individual assets included in the household survey (radio; TV; telephone; refrigerator; iron or wooden bed; cart; bicycle; motorbike or scooter; car, truck or tractor) and publicly provided resources, such as electricity, water and toilet facilities.

Contributions [4, 5], relate an investigation on digital image analysis using principal component analysis (PCA) based on singular value decomposition (SVD) for detection of burned areas.

Four factors were extracted using Principal Component Analysis (PCA) based on the condition "eigenvalue > 1" explaining 79.99% of the total variation.