Omics for prediction and prognosis
Using molecular data to predict who will get a disease and how it will progress
Using molecular data to predict who will get a disease and how it will progress
In this workstream we use large, complex molecular (‘omics’) datasets to identify biomarkers to predict who will get a disease and how it will progress.
We use machine learning to identify, optimise and validate these molecular biomarkers. We then combine them with data from health records, cohort studies and trials to develop disease prediction tools for use in a range of settings.
Our biomarker identification work will support other NIHR Bristol BRC themes, including respiratory and mental health.
Improving decisions on what to focus on in research using large datasets
Theme Translational data science
Workstream Large, complex datasets
Exploring inflammation as a driver for post-operative complications
Theme Translational data science
Workstream Omics for prediction and prognosis
Investigating new approaches to drug development using human genetics
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring the link between obesity and heart failure using genetics
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Lung development in early life and respiratory diagnosis and treatment
Themes Respiratory disease Translational data science
Workstream Exacerbation prediction and aerosol emissions
Can we use DNA methylation to predict disease in diverse populations?
Theme Translational data science
Workstream Omics for prediction and prognosis
South West Secure Data Environment
Theme Translational data science
Workstream Clinical informatics platforms
Preventing cardiovascular events in stroke patients
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring the link between genes and cognitive decline
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Do ethnicity and coexisting health conditions impact high-risk diabetes?
Theme Translational data science
Workstreams Clinical informatics platforms Large, complex datasets
Handling missing data in large electronic healthcare record datasets
Theme Translational data science
Workstream Large, complex datasets
Biomarkers for screening and diagnosing lung cancer
Theme Translational data science
Workstream Omics for prediction and prognosis
Treatment resistance and drug side effects in schizophrenia
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring how obesity influences cancer survival
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Using biomarkers and machine learning to predict antidepressant resistance
Theme Translational data science
Workstream Omics for prediction and prognosis
Can DNA methylation biomarkers predict whether pleural effusion is caused by cancer?
Theme Translational data science
Workstream Omics for prediction and prognosis
Using DNA methylation biomarkers to understand Parkinson’s disease severity and progression
Theme Translational data science
Workstream Omics for prediction and prognosis
Data driven approaches to drug target prioritisation
Theme Translational data science
Workstream Genetic evidence to prioritise intervention