"This study presents a comprehensive comparison of machine learning algorithms for developing metabolomic aging clocks, benchmarking a wide range of models under consistent conditions in one of the largest metabolomics datasets available globally," wrote IoPPN lead author Dr. Julian Mutz, with co-authors Raquel Iniesta and Cathryn M. Lewis.
"Metabolomics is the scientific field that studies the chemical substances produced by an organism, cell, or tissue as a result of metabolism, called metabolites, which sustain life. Catabolism breaks down complex molecules; anabolism synthesizes them."
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