library(plotly)
#load the dataset
metabolic_measures <- synthetic_metabolic_dataset
phenotypes <- synthetic_phenotypic_dataset
#Pre-process the metabolic features
prepped_met<-QCprep(as.matrix(metabolic_measures), MiMIR::PARAM_metaboAge)
#Apply the metaboAge
metaboAge<-apply.fit(prepped_met, FIT=PARAM_metaboAge$FIT_COEF)
age<-data.frame(phenotypes$age)
rownames(age)<-rownames(phenotypes)
scatterplot_predictions(age, metaboAge, title="Chronological Age vs MetaboAge")
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