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We used the proposed method, an exhaustive search, and the GHC method, and compared the estimation time for from 3 to 70 genes.
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Section 4 analyzes and compare the estimation performance under the various MACs.
AE conceived the RSM and ANN design and analysis, compared the estimation capabilities of the RSM with ANN and drafted the manuscript.
The logistic procedure fits linear logistic regression models for binary or ordinal response data using Maximum Likelihood estimations and compares the estimated samples whereas artificial neural network systems attempts to assign proper weights to the respective inputs by a 'genetic algorithm' optimization procedure to allow for the correct deduction of the ultimate outcome.
AE conceived the ANN design and analysis, compared the estimation capabilities of the RSM with ANN and drafted the manuscript.
With the data split into training subsets and test subsets, we compared the estimation accuracy.
We compared the estimation accuracy and suitability for confirmatory testing of autocorrelation, discrete Fourier transform (DFT), integer period discrete Fourier transform (IPDFT) and a previously proposed Hybrid measure.
We also compared the estimation performance using back-propagation neural networks (BPN) and statistical response surface methodology (RS M.
We compared the estimation results of the exhaustive search with the results of the proposed method and the GHC method.
In this prospective, double-blinded, randomized controlled trial, we determined the agreement of measured and estimated serum osmolality during theoretically equiosmolar load of mannitol and HS, and compared the accuracy of serum osmolality estimation derived from four common used formulas between the two agents.
First, we repeated the quality estimation test and compared the proposed directional power ratio measure (18) with the aforementioned reference measures.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com