Anastasia Godneva

The paper examines diet-oral microbiome associations using metagenomic data from 5,824 participants, finding shared microbial signatures across dietary patterns.

The paper develops a mathematical model and formula (HOMA-C) to quantify beta cell carrying capacity in prediabetes, showing significant variation and its association with diabetes risk.

This study characterizes the oral microbiome across 9,431 participants and identifies associations with metabolic health, finding specific microbial features linked to metabolic traits and disease classification.

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 study maps gut microbiome associations with 37 health conditions using the Human Phenotype Project 10K cohort, identifying significant microbial abundance patterns linked to favorable health states.

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

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.