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The most popular way of creating such models is by approximation of sampled EM-simulation data using, for example, low-order polynomials, support vector regression or neural networks.
As a test, I ran the regressions based on single-imputed data using for all model specifications only those observations for which estimation of the full specification (cf. column 4 of Tables 1 and 2) is possible; the results are substantially stable.
Data transfer is synchronous that is, there is some real time processing of patient data using, for example, automated algorithms to interpret the data.
Metabolomics data from breast cancer patients can be used in a similar way to gene expression microarray data, using, for example, hierarchical clustering and heat maps.
A handful of recent algorithms (Allen and Liu, 2013; Gallopin et al., 2013) have considered Markov networks for non-Gaussian data, using for example the Poisson distribution for RNA-sequencing read counts.
The traditional way of doing this would be to carry out some kind of ad hoc normalization for the count data and then fit the model responses to the normalized data using for example least squares or maximum likelihood methods.
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View the data used for this article.
The Paul Cycle is the canonical data used for designing implants.
S.M.M. and M.H. curated the data used for the analyses.
The data used for this analysis are quantitative and qualitative.
I provided Mr. Okrent with the data used for that calculation.
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