Alberto Santos Delgado

Dr. Alberto Santos Delgado serves as Research Group Leader of the Multi-omics Analytics Group at the Big Data Institute (BDI) within the Nuffield Department of Medicine at the University of Oxford. He earned his PhD in computational biology from the University of Copenhagen and leads research at the intersection of network biology and clinical data science.

His research program focuses on developing frameworks for integrating and analyzing clinical multi-omics data, particularly proteomics and metabolomics, to identify disease markers and therapeutic opportunities. The Multi-omics Analytics Group leverages network biology approaches to harmonize heterogeneous clinical data through relationship mapping, applying graph algorithms to mine patterns and explain system behavior. A key innovation in his work involves building graph-based definitions of biomedical entities—diseases, patients, tissues, and pathogens—through connections to molecular and clinical data, enabling novel insights into disease comorbidities and patient similarity.

Dr. Santos Delgado's recent publications demonstrate a strong trajectory in applying proteomics to fundamental biological questions and clinical applications, particularly in liver disease research. His work in Nature (2020) explored proteome landscapes across biological kingdoms, while his Molecular Systems Biology paper (2019) identified novel proteins associated with non-alcoholic fatty liver disease, revealing potential biomarkers like polymeric immunoglobulin receptor (PIGR) that showed significant elevation in both NAFLD and cirrhosis.

The Multi-omics Analytics Group under Dr. Santos Delgado's leadership represents a cutting-edge approach to biomedical data science, developing methodologies that bridge computational biology with clinical applications. Their network-based frameworks have significant potential for advancing precision medicine through improved disease classification and the discovery of novel therapeutic targets by identifying disease-specific network patterns in complex biological systems.