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An experimental analysis based on clinical trial data from 200 volunteers has been conducted to illustrate the effectiveness of this method.
This study aimed to predict the 6-year incidence of metabolic syndrome (MetS) using an artificial neural network (ANN) system and multiple logistic regression (MLR) analysis based on clinical factors, including the insulin resistance index calculated by homeostasis model assessment (HOMA-IR).
Of the 2092 participants selected for analysis based on clinical characteristics, genotyping was performed on the 1984 with sufficient quantity and quality DNA.
Categories were built prior to analysis based on clinical judgement.
Additional genes were selected for sequence analysis based on clinical findings.
First, we used an analysis based on clinical cases to predict the stability of the fixation.
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The heterogeneity of the studies will be assessed before meta-analysis based on clinical similarities in populations and interventions.
For the case/control association analysis based on AD clinical diagnosis, logistic regression was performed in PLINK under both additive and genotypic models, and again including covariates for age, gender, and education.
We conduct our analysis based on direct clinical comparisons.
For subcategory analysis based on the clinical stage, six clinicopathological parameters are all correlated with ALDH1A1 expression in the NA group.
In some families, complex statistical analysis based on available clinical and pedigree data may be required to provide the most accurate characterization of genetic risks.
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analysis based on DS1-bolon
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