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Bayesian statistical data integration of single-cell and bulk OMICS datasets for accurate prediction of clinical outcomes in rheumatoid arthritis

   College of Medicine, Veterinary and Life Sciences

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  Dr S Siebert, Dr Thomas Otto  No more applications being accepted  Funded PhD Project (UK Students Only)

Glasgow United Kingdom Data Analysis

About the Project


Prof Stefan Siebert

Dr Thomas D. Otto 

PhD project summary: 

Precision medicine has the promise to help the prediction and understanding of diseases. One challenge is generating computational methods to combine and analyse biological (including genomics, Transcriptomics and proteomics) with clinical data. In this project the student will develop new statistical methods to combine biological and clinical data (such as age, smoking status, phenotype of disease and drug treatment) of immune mediated inflammatory diseases, focusing on rheumatoid arthritis as exemplar. The aim is to combine different datasets and cross reference them through Bayesian statistical framework in the latent space. This project will be supervised by an academic clinician (Stefan Siebert), a statistician (Mayetri Gupta) and a bioinformatician (Thomas Otto), reflecting the interdisciplinary nature of this proposal. 

Funding Notes

The studentship provides funding for tuition fees, a stipend and laboratory expenses for Home (UK) applicants only.
The MVLS/EPSRC grant provides tuition fees and stipend of at least £15,285 (UKRI rate 2020/21).
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