FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW

Machine-learning to create predictive models of genetic regulation

   Faculty of Biological Sciences

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Prof David Westhead, Dr J Boyes  No more applications being accepted  Competition Funded PhD Project (European/UK Students Only)

About the Project

Genetic regulatory networks control critical biological processes, from development and cellular differentiation to the response to environmental cues and stress. In eukarytotes however the networks are substantially unknown and difficult to predict, since they may differ substantially in different cell types and because enhancer or supressor activities for individual genes may be driven from distal genomic regions. In recent years there has been an explosion in high throughput data generation relevant to genetic regulation. This has been primarily based on next generation DNA sequencing, and includes DNase/ATAC-seq, ChIP-seq for chromatin modification and transcription factor binding, RNA-seq for gene expression measurement and increasingly 3D chromosome structure data. This project aims to apply machine learning methods to these large, heterogeneous data sets to predict genetic regulatory networks. It follows on from recent work in the group, where we have developed penalized regression methods integrate DNase-seq, ChIP-seq and RNA-seq data into regulatory models for individual genes. It will employ methods such as neural networks and support vector machines, where we have existing expertise, and an important aspect of the project will be assessment of the utility of the prediction models in analysing the biological consequences of regulatory (non-coding) DNA changes, for instance in genome wide association studies of disease phenotypes and the interpretation of mutational data, for instance from cancer cells.

Funding Notes

Project is eligible for funding under the BBSRC White Rose DTP: Doctoral Studentships in Artificial Intelligence, Machine Learning and Data Driven Economy.

Successful candidates will receive funding for 4 years, covering UK/EU fees and research council stipend (£14,777 for 2018-19).

Candidates should have, or be expecting, a 2.1 or above at undergraduate level in a relevant field. If English is not your first language, you will also be required to meet our language entry requirements. The PhD is to start in Oct 2018.
Apply online:
Include project title and supervisor name, and upload a CV and transcripts.


Toenhake CG, Fraschka SA-K, Vijayabaskar MS, Westhead DR, van Heeringen SJ, Bártfai R Chromatin Accessibility-Based Characterization of the Gene Regulatory Network Underlying Plasmodium falciparum Blood-Stage Development. Cell Host&Microbe 23 557-569, 2018.
Zainul Abidin FN, Westhead DR Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer. Nucleic Acids Res 45 e53-, 2017.
Shar NA, Vijayabaskar MS, Westhead DR Cancer somatic mutations cluster in a subset of regulatory sites predicted from the ENCODE data Molecular Cancer 15, 2016.
Goode DK, Obier N, Vijayabaskar MS, Lie-A-Ling M, Lilly AJ, Hannah R, Lichtinger M, Batta K, Florkowska M, Patel R, Challinor M, Wallace K, Gilmour J, Assi SA, Cauchy P, Hoogenkamp M, Westhead DR, Lacaud G, Kouskoff V, Göttgens B, Bonifer C Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation Developmental Cell 36 572-587, 2016.

How good is research at University of Leeds in Biological Sciences?

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities
PhD saved successfully
View saved PhDs