Alec Eames

This study shows that old white adipose tissue can rejuvenate in young hosts through thermogenic remodeling and pathway activation, reversing biological age markers.

This study identifies aging genes in human blood by integrating epigenetic and transcriptomic data, finding genes enriched for immune functions that better replicate across populations and link to aging outcomes.

The paper develops Biolearn, an open-source framework for curating and evaluating aging biomarkers across datasets, finding that harmonization and standardization improve their performance and generalizability.

This paper introduces LongReadAge, a probabilistic model for predicting epigenetic age at the cell-type level using long-read sequencing data.

MethylGPT is a transformer-based model trained on human DNA methylation data that accurately predicts methylation values and improves age and disease prediction.

The paper uses machine learning on plasma proteome data to develop organ-specific and conventional aging models, showing that age-related diseases reflect accelerated aging of specific organs and systems.

The paper uses dynamic metabolic modeling to analyze time-course multi-omics data from murine pro-B cells, revealing key metabolic changes during the cell cycle.