Eugene Lane

The paper develops a deep learning model to predict human biological age from gut microbiome data and identifies key microbial biomarkers associated with aging.

The paper develops a deep-learned age predictor from transcriptomic data and shows that batch effects can obscure age-related changes.

The paper develops a deep learning pipeline to predict clinical trial outcomes using transcriptomic data, achieving 83% cross-validated accuracy and outperforming gene-based classifiers.