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.
Identifying different types of nephrotic syndrome using blood cell DNA
Theme Translational data science
Workstream Omics for prediction and prognosis
Exploring inflammation as a driver for post-operative complications
Theme Translational data science
Workstream Omics for prediction and prognosis
Can we use DNA methylation to predict disease in diverse populations?
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
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