New studentship on 'Integrating genomic and electronic health data for drug-discovery'

This is a 4-year full-time PhD studentship funded by Engineering and Physical Sciences Research Council (EPSRC) industrial CASE studentship, supported through the National Productivity Investment Fund (NPIF). Funding covers university course fees and an annual maintenance stipend. 

Drug discovery process faces major challenges that threat their sustainability, and this has wider implications to society in general. A major problem is that standard pre-clinical models for drug-target selection and validation had a poor predictive capacity to corrective identify valid drug-targets. An alternative way to improve the process of drug-target selection and validation is to use large-scale human genomics in conjunction with other omics (e.g. proteomics, radiomics) and electronic health record (EHR). Previous proof-of-concepts developed by Professors Casas and Whittaker (e.g. Nelson et al. Nat Gen 2015 and Finan C, et al Sci Transl Med 2017) have shown the great potential for this strategy. However, a systematic, scalable and computational robust integration of genetic, other-omics and EHR data poses multiple methodological challenges: 

1. The use of complex, longitudinal data from multiple sources to stratify patients by their disease trajectories or response to treatment, in order to use these strata to determine more precisely the phenotypic consequences of genetic variation and hence suggest drug targets that enable precision medicine, or more generally, the definition of phenotypes relevant to drug discovery eg focused on disease progression rather than occurrence.
2. The integration of EHR data with ‘omics data (expression, protein etc) to understand the causal pathway from genetic variant to disease endpoint This PhD studentship aims to contribute to the development and application of novel analytics that capitalise on the integration of Big Data in the form of national electronic health record, genomics and other omics (including imaging) to improve the selection and validation of drug-targets.  

In this project we will develop methods to solve these and related questions. By first intent we will work in the Bayesian inference framework, but less principled machine learning approaches will be investigated if appropriate. 

The student will be based at the UCL Institute of Health Informatics (IHI) at 222 Euston Road, London NW1 2DA. Benefits from being at IHI are multiple, including: 

• Access to large-scale resources, is already in place 
• A multi-disciplinary environment of >40 staff members with expertise on bio/health-informatics, data science, and clinical academics, as well as >28 PhD students in training. 
• Highly interactive research teams in areas relevant to the studentship
• World class groups in the area of health informatics, genomics and drug-discovery. 

Application Deadline: 30th August 2017

Primary supervisor: Professor JP Casas, UCL

Subsidiary Supervisor: Dr David Prieto-Merino, UCL

Industrial Supervisor: Professor John Whittaker, GSK 

Host department/institution: UCL Institute of Health Informatics

Interview date: 8 September 2017

For further information and how to apply, please visit UCL Human Resources.