ComputationalAgingLab/ComputAge
A library for full-stack aging clocks design and benchmarking.
This tool helps researchers and bioinformaticians working on aging research to develop and rigorously test new 'aging clocks.' It takes DNA methylation data (specifically CpG site levels) from biological samples and uses it to predict an individual's chronological and biological age. The output helps evaluate how well new aging clock models perform against established benchmarks, especially in predicting age acceleration in specific health conditions.
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Use this if you are a researcher developing or evaluating machine learning models designed to predict age from DNA methylation data and need a standardized way to benchmark their performance against known aging-accelerating conditions.
Not ideal if you are a clinician looking for a diagnostic tool for biological age or if your research doesn't involve epigenetic data.
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Jupyter Notebook
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CC-BY-SA-4.0
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Last pushed
Jan 29, 2025
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