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Big Data & Computational Science

Led by Prof Cornelia van Duijn and co-led by Prof Blanca Rodriguez, this theme builds on our capabilities in data analysis for population cardiovascular health, including a new focus on vascular dementia. We are working in collaboration between cardiologists, engineers and computer scientists to extend upon big data techniques, artificial intelligence, modelling and simulation. These will provide new insights into treatments for cardiovascular disease by integrating and expanding data from multiple sources including the ECG, wearables, and new imaging modalities trained on –omics data.

Machine learning model using blood proteomics information to estimate a proteomics age clock for a large sample of participants from the UK Biobank

Proteomics captures age-related pathology. Figure shows the cumulative incidence of mortality and vascular diseases during 11-16 years of follow up in the UK Biobank.

Overview

We have already achieved break-throughs in Big Data generation and computational analysis of multimodality datasets from -omics and imaging to electronic health records. Our new challenges will be in expanding effective, cross- disciplinary collaborations, establishing novel patho-physiological disease signatures and mechanisms, non-invasive markers, and harnessing Big Data to identify safe and effective therapies for cardiovascular disease and related disorders, including dementia.

Combining and analysing large, multi- modal data will enable our researchers to dissect disease patho-physiology, discover and validate new targets, enhance risk assessment and support selection of optimal treatments, in synergy with the DD&D and R&R Themes. The research focus of the CRE on vascular, cardiac and metabolomic pathology will be expanded to multi-organ dysfunction of the heart-brain axis, linking the BHF CRE with Dementia Research Oxford to address this major new priority for the CRE.

Sub-Theme 1: MULTI-OMICS TO PHARMACO-OMICS (LEAD: HOPEWELL)

This sub-theme will capitalise on rapidly expanding multi-dimensional -omics and electronic healthcare data of the CRE’s large, deeply-phenotyped studies, clinical trials and multi-disciplinary collaborations. Our researchers are leveraging data from across the globe to discover novel disease mechanisms and causal risk factors, create biologically informed disease signatures for vascular, cardiac, metabolic and brain pathology, and identify multi-omic predictors of response to cardiovascular therapies (pharmaco-omics). Our novel insights will support development of tools for risk prediction and stratification, target discovery (DD&D theme) and precision therapeutics.

Sub-theme 2: AI IN PATHOPHYSIOLOGY (LEAD: DOHERTY)

This sub-theme aims to bring together world-leading computational and medical research to develop new multi-modal artificial intelligence tools to enable discovery and validation of new pathways for intervention in the fight against cardiovascular disease.

We will build on rapid developments in AI and high-performance computing to exploit, integrate and expand imaging and functional data from electrocardiography and wearable devices to dissect cardiovascular patho- physiology and disease.

SUB-THEME 3: AI-ENHANCED IMAGING (LEAD: ANTONIADES)

Our researchers will develop novel AI-enhanced imaging biomarkers for risk stratification, early diagnosis and precision therapeutics.  AI/computer science will be used to train disease signatures from routine clinical images (CMR, CT, U/S) to best match the biological ground truth in the tissue of interest. We will validate these biomarkers in large longitudinal imaging cohorts such as the ORFAN study, against a wide range of clinical outcomes and hard endpoints.  Human tissue from large cohorts are available for deep tissue phenotyping using spatial / bulk RNA transcriptomics and multi-omics analysis to define the biological ground truth for existing and novel therapeutic targets discovered in the DD&D Theme.  The following cohorts contribute tissue and imaging for the modelling:

  • Ox-HVF provides arteries, myocardium and adipose tissue (AT) which will be linked with U/S and clinical /ex vivo CT images).
  • STARR and STICS trials provides atrial tissue collection, which will be linked with CMR & U/S images.
  • Oxford Biobank provides AT biopsies which will be linked with CMR and U/S images.

Theme Lead

Co-Lead

Sub-Theme Leads

There are no team members found