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ACRM implemented SAAPdap and the remaining enhanced analysis methods, revised the paper and contributed to the machine learning.
The other compared methods included the node-based approach using greedy search (jActiveModules), GXNA (Gene eXpression Network Analysis) [ 8], several methods revised from ClustEx and the simple DE gene approach with minimum fold change (FoldChange_ [fold]).
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All authors contributed to discuss on the method, revised the manuscript and approved the final version.
was used to estimate the total AO potential of the RILs and RIL parents grown in the glasshouse and controlled environment experiments, according to the method revised by Payne et al. (See Supplementary Information for details).
The method, "Revised Quantitative Insulin Sensitivity Check Index" (RQUICKI) is based on plasma concentrations of glucose, insulin and free fatty acids (FFA) and it generates good and linear correlations with different estimates of insulin sensitivity in human populations.
The method, "Revised Quantitative Insulin Sensitivity Check Index" (RQUICKI) implies a valuation of the homeostatic energy balance and is based on plasma concentrations of glucose, insulin and free fatty acids (FFA).
Our proposed method revises the motion information based on the characteristics of the 3D visual perception.
<img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0001181.e001.PNG" class= inline-graphic"/> The MHW method revises estimates recursively as new observations accumulate ad infinitum with Equations 2, 3, and 4 where α, β, and γ are the smoothing pseudo-parameters for lt, rt, and sm| t, respectively, as defined in the Results section.
Similar to the classic PTD method, our revised PTD method also consists of three core steps: parameter specification, seed point selection and initial TIN construction, and iterative densification of TIN.
We calculated the estimated ESR using the simple product-based method and revised product-based method for each of the 1,000 pairs of samples.
This paper generalizes the SES method into a new adaptive method called revised simple exponential smoothing (RSES), as an alternative method to recognize non-stationary level shifts in the time series.
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