Transcriptomic Age Prediction Using Mixture-of-Experts Models Reveals Tissue-Specific Aging Signatures

We develop a mixture-of-experts ensemble clock for transcriptomic age prediction, trained on 57,584 samples spanning 28 tissues (ages 1-114 years). The model achieves R2 = 0.854 and MAE = 4.26 years with calibrated uncertainty intervals, and reveals tissue-specific aging signatures including CDKN2A/p16, AMPD3, MIR29B2CHG, and SEPTIN3.