Eran Segal

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.

This paper evaluates image-to-image diffusion for generating privacy-preserving synthetic medical images, finding it outperforms text-to-image methods in fidelity and clinical signal preservation.

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.

RobPicker improves macromolecule identification in cryo-ET by addressing label noise and class imbalance through a meta-learning framework, outperforming existing methods in detecting small and rare species.

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 study constructs pangenomes of 728 human gut prokaryotic species, revealing genetic diversity linked to stress response, antibiotic resistance, and host characteristics.

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

The paper introduces StrainPGC and MIDAS v3 to accurately estimate intraspecific microbial gene content variation in metagenomic data, outperforming existing methods and revealing functional diversity in gut microbiomes.

The paper constructs a microbiome-phenotype atlas using large cohorts from Israel and the U.S., showing robust associations and high prediction accuracy across continents.

The paper estimates the heritability of metformin's glycaemic response using EHR data and population pedigrees, finding a heritability of 10.5-12.2%.

The paper identifies non-coding variants in the IL18RAP 3'UTR that reduce ALS risk by modulating neuro-inflammation through NF-κB signaling in microglia.

The paper identifies 31 host genetic loci influencing gut microbiome composition through GWAS and suggests causal effects in inflammatory diseases.

The paper develops a high-throughput method to assess ribosomal frameshifting potential and identifies factors influencing frameshifting rates in HIV and other organisms.

This study compares microbiome and genome-wide association study predictive power for 13 diseases, finding microbiome indicators outperform genetic variants in disease discrimination.

The paper uses a designed sequence library to study splicing regulation and cell-to-cell variability in splicing decisions.

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

The paper identifies methylation patterns specific to intestinal epithelial cells to detect cell-free DNA in plasma, showing increased levels in advanced CRC compared to IBD and PDAC.

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.

The paper measures promoter activity in budding yeast under varying TF concentrations and shows that competition between binding sites, not site number, determines gene expression, with a thermodynamic model explaining the results.