Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.

ABSTRACT: Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological aging at the molecular level. We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. We apply this framework to one of the largest single-cell transcriptomic datasets encompassing over a million immune cells from 981 donors, revealing a ribosomal gene subnetwork, whose expression correlates with age independently of cell

SUBMITTER: Li ZP 

PROVIDER: S-EPMC11814351 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature