Carles Foguet

The paper improves metabolomic biomarker discovery in admixed American cohorts by training models on diverse data, achieving better predictive performance and more significant associations with cardiometabolic diseases.

OmicsPred collects 3,339,469 genetic prediction models for transcript, protein and metabolite traits with harmonised metadata and MetaXcan/PGSC-Calculator files, plus multi-ancestry Million Veterans Program phenome-wide association results.

This study identifies genetic determinants of plasma protein variance across ancestries and shows their impact on cardiometabolic disease risk.

This study identifies metabolic reaction fluxes that interact with CAD risk alleles, revealing their role as amplifiers or buffers in disease susceptibility.

The paper develops genetic scores for 17,227 molecular traits using multi-omic data from INTERVAL and validates them across ancestries, demonstrating their utility in pathway analysis and disease association discovery.

The paper develops genetically personalised organ-specific metabolic models to identify associations between metabolic fluxes and disease risk, finding 97 fluxes linked to coronary artery disease.

The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.