Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Dr Alberto Santos Delgado

Dr Alberto Santos Delgado

Alberto Santos Delgado

PhD


Research Group Leader, Multi-omics Analytics Group

I am a research group leader at the Big Data Institute (BDI) within the Nuffield Department of Medicine. I obtained my PhD in computational biology at the University of Copenhagen (Denmark) and my research focus is on network biology as a means to understand the complexity of disease and health.

The Multi-omics analytics group works on the integration and analysis of clinical multi-omics data, mainly proteomics and metabolomics, to identify markers of disease, prognosis and treatment. In a time where large amounts of heterogeneous data are being generated, integration is key to interpret complex problems and it requires frameworks capable of harmonizing and analysing these data. This is especially relevant in clinical research. To facilitate integration of multidimensional complex data, the group makes use of network biology, which provides tools to seamlessly integrate data through relationships and brings with it the full potential of graph algorithms. These algorithms can mine those relationships to identify patterns, explain system behaviour and support inference. Further, networks can improve machine-learning models by incorporating network topology as relevant information for prediction and classification. The ultimate objective of the group is to build a graph-based definition of biomedical entities, that is, the definition of a disease, a patient, a tissue or a pathogen through connections to other entities such as proteins, metabolites, transcripts or diagnoses derived from multi-omics and clinical data. These distinctive networks can then be used to build new relationships such as disease comorbidities or patient similarity that could unravel treatment opportunities.

Recent publications

More publications