Research Activities
Genome-wide association studies (GWAS) for the identification of germline variants associated with diseases, other binary phenotypes and quantitative traits.
Transcriptomic analysis of cancer cells, stem cells, organoids, human and murine tissues in disease models
Identification of genetic and regulatory variants influencing drug response and adverse events
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.
