Network-based approaches are increasingly relevant as a computational-biological paradigm for Precision Medicine, enabling the systematic incorporation of evidence from full-genome sequencing, genome-wide association studies, and other empirical sources into the investigation of disease, including potential drug and intervention targets1. This paradigm poses significant methodological and computational challenges. As more data become available, the analysis of such large-scale complex networks is hindered by limitations of existing statistical tools. While graph-theoretic and machine learning methods have been employed in a number of areas, new computational methods are needed which scale to meet the prediction and model-explanatory requirements of large, high-dimensional datasets.
Aims
This PhD will explore new methods that combine knowledge of network properties and Bayesian learning in order to tackle these challenges. In cooperation with other teams, these methods will be tested on different applications relevant to Precision Medicine, such as the stratification of patient-derived data for enabling clinically-oriented decisions and therapeutic research in cancer and neurodegenerative diseases. The student will:
- Investigate the application of embedding methods which have been applied to large-scale natural language processing2,3 and more recently to the analysis of web data4, 5, 6 to molecular networks.
- Devise inference methods based on these representations which improve upon existing methods for the identification of influential network nodes or edges, network modules, and other network-based properties of biological significance.
- Investigate application cases in oncology and neurodegenerative disease research, which require the analysis of multiple sources of high-dimensional data, such as multi-omics datasets or different layers of information relevant to predicting patient outcomes.
We invite applications from candidates with a good first degree (and ideally a Master's) in bioinformatics, computer science or related disciplines.
Applicants must meet the entry requirements for acceptance to the University of Edinburgh PhD programme. See:
www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=924
UK/EU tuition fees only (any eligible non-EU candidates must fund the remainder of the overseas tuition fee).
Via www.jobs.ac.uk/job/BFO059/3-year-funded-phd-in-biomedical-informatics