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Subsequently, an optimisation procedure is applied to find the combination of gates and the parameters that best explain the data, by minimising an objective function that quantifies the difference between data and simulation, while penalising model size.
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For the remaining components, the described template-matching procedure was applied to find the best-fit component of the default mode network.
Secondly, a selection procedure was applied to find the significant variables for each gene.
The same procedure was applied to find predictors for airway symptoms, need for oxygen treatment and bleeding manifestations.
Second, a variable selection procedure was applied to find significant variables for each gene, for which a stepwise regression was employed (step.method = "two.ways.backward", α = 0.01).
Secondly, a variable selection procedure was applied to find significant variables for each gene, for which we employed a stepwise regression (step.method = "two.ways.backward", alfa = 0.01).
The same procedure using the laboratory informatics system was applied to find the number of positive samples by time distribution.
A heuristic procedure, the hybrid Greedy Randomized Adaptive Search Procedure (HGRASP), is applied to find Pareto optimal solutions.
A similar procedure is applied to subjects found to have a large adenoma.
A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency.
Finally, to find these energy minima starting from pre-defined activation pattern, a gradient decent procedure is applied to the energy function.
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