ComputAgeBench: Epigenetic Aging Clocks Benchmark
Keywords: biological age, epigenetic aging clocks, DNA methylation, aging biomarkers, longevity
TL;DR: ComputAgeBench is the first framework for benchmarking aging clocks, which comprises 66 open-access datasets and compares 13 published models to find reliable biomarkers of health and aging.
Abstract: The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages and tested 13 published clock models. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging.
Supplementary Material: zip
Primary Area: datasets and benchmarks
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Submission Number: 7894
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