ORANGE: A Machine Learning Approach for Modeling Tissue-Specific Aging from Transcriptomic Data

Despite aging being a fundamental biological process which profoundly influences health and disease, the interplay between tissue-specific aging and mortality remains underexplored. This study applies machine learning on GTEx transcriptomic data to model tissue-specific biological ages across 12 different types of tissues and introduces an age-gap metric to quantify deviations from the chronological age. We use several modeling techniques optimized with three feature selection strategies: Pearson correlation, age-related differentially expressed genes, and tissue-enriched genes (expressed at least fourfold higher in a specific tissue). Among these, Pearson correlation combined with elastic net regression yields the best performance, with models achieving an average RMSE of 6.44 years and an R ² of 0.64. To quantify deviations from chronological age relative to the population, we train neural networks to regress predicted ages against chronological ages, and subtract their outputs from the predicted ages to calculate a metric which we call the age-gap . Age-gap statistics reveal significant tissue-specific aging patterns, identifying extreme agers and correlations between extreme aging and mortality. About 20% of subjects are found to exhibit extreme aging in one tissue, while 1% show multi-organ aging. Further analysis reveals that accelerated aging in specific tissues correlates with with greater risk of death from illness. These findings greatly emphasize the role of transcriptomics in aging research and its implications for health and longevity.