Computational Biomedicine

Our research focuses on the development and application of Computational Biomedicine and Systems Biology approaches for the characterization of human diseases. We integrate biomedical data from different sources and at multiple scales through multimodal and multi-omics analyses, biological network analysis, and mechanistic modelling, combined with artificial intelligence approaches, to characterize molecular alterations, identify disease-associated mechanisms, establish patient stratification patterns, and discover novel biomarkers and therapeutic targets.

1. Study of the molecular mechanisms linking ageing to the development and progression of neurological, oncological, and metabolic diseases using single-cell approaches.
2. Characterization of the molecular basis of neurological diseases:
   a) Generation and integration of spatial and single-cell atlases to elucidate sex differences in neurodegenerative diseases (Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, and multiple sclerosis); b) Comparative analysis of gut microbial communities and their metabolic profiles in men and women with neurodegenerative diseases; c) Development of descriptive and predictive models for improved patient stratification in neurological disorders through the integration of multimodal data.
3. Identification of novel biomarkers in cancer: a) Development and application of integrative multi-omics data analysis methods for the identification of novel biomarkers of diagnosis and tumour progression; b) Investigation of the neurobiology of cancer and characterization of the role of the nervous system in cancer.
4. Clinical predictors in metabolic disorders: Identification and prioritization of potential therapeutic targets in obesity, diabetes, and metabolic dysfunction-associated fatty liver disease (MAFLD) through large-scale omics data analysis and the application of artificial intelligence methods.