Adina Weinberger

The paper analyzes intra-species genetic diversity of gut bacteria across individuals and links strain-level variations to host physiological traits.

This paper develops self-supervised learning models for gut microbiome metagenomic data, showing improved prediction performance in data-limited and cross-cohort settings.

This paper uses self-supervised learning to analyze ECG data and identifies genetic associations with cardiovascular state changes.

The paper systematically assays core promoter elements to understand their role in transcriptional regulation and identifies key sequence features and spatial organization principles.

This study shows environmental factors have a greater impact on human gut microbiota than host genetics, with microbiome data adding significant predictive value to phenotypes.