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.
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
Biomarkers for screening and diagnosing lung cancer
Theme Translational data science
Workstream Omics for prediction and prognosis
Creating the infrastructure to enable translational data analysis at scale
Theme Translational data science
Workstreams Clinical informatics platforms Large, complex datasets
Data driven approaches to drug target prioritisation
Theme Translational data science
Workstream Genetic evidence to prioritise intervention