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In addition, by introducing multiple sample classification, the genotyping estimation takes advantage of several SNPs other than the current one to be more robust.
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This is especially useful for multiple sample comparisons; (ii) Gene classification was based on multiple comparison.
In the next step, the user can forward the extracted pathway variance data to a clustering module, for identifying sample groups with similar expression variance across multiple pathways, or to a classification module (for labelled data), to build models for sample classification.
This sample classification method is designed, through multivariate analysis, to construct classification trees recognizing a complex pattern of multiple peak intensities.
Automatic classification was successful for all but four samples out of 14,400 classification trial configurations, representing 403,200 sample classification attempts (28 attempts per configuration).
The results appeared to correlate well with the sample classification.
Only a few markers were needed for good sample classification.
To remove any bias of this high proportion of non-remitters in the replication cohort on the predictive accuracy in this cohort and to provide a distribution and confidence interval of this classification accuracy, a multiple sampling bootstrap analysis procedure was employed.
The model, however, provides a natural technique for integrating missing data and multiple samples at the same time, and produces a dichotomous classification associated with EFS.
Given multiple samples, Parallel-META could parse the result of all samples together and map the classification information of one common phylogenetic tree.
Sometimes multiple samples from the same lot of flour give different results.
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