Research Activities
Genome-wide association studies (GWAS) for the identification of germline variants associated with diseases, other binary phenotypes and quantitative traits.
Genome assembly of high resolution, haplotype resolved genomes and polymorphism characterization
Application of advanced statistical methods, machine learning, deep learning, explainable AI (XAI), and data mining for predicting pathological mechanisms, identifying disease biomarkers and supporting patient stratification.
Development, application and optimization of automated pipelines for the analysis of genomics, epigenomics and transcriptomics data for large-scale studies.
Management and harmonization of biomedical data through organization, curation, and integration of heterogeneous health and research datasets into standardized formats.
Implementation of studies that combine classical epidemiological methods with high-throughput technologies to reveal the intricate relationship between molecular profiles, environmental exposures, and disease risk.
