Article

Transcriptomic age prediction using mixture-of-experts models reveals tissue-specific aging signatures in large-scale human RNA-sequencing data

2025-06-30

Abstract excerpt

Transcriptomic age prediction has emerged as a powerful approach for understanding biological aging processes, yet systematic comparisons of large-scale RNA-sequencing datasets remain limited. We developed and validated a mixture-of-experts machine learning model using the ARCHS4 dataset comprising 56,877 human RNA-sequencing samples spanning ages 2-114 years across diverse tissues. Our model achieved superior per...

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Literature Corpus work: 25fc8ae8-508f-5871-aa0e-cc259f8e448f
DOI: 10.1101/2025.06.28.25330474

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