... understanding life in molecular detail

Prof David Westhead

Bioinformatics, Computational Biology, Machine learning, Statistics


The group is interested in prediction methods for biological problems based on machine learning and statistical methods. Recently we have worked on predicting protein structure, protein-protein interaction sites, gene function and the functional effects of single nucleotide polymorphisms. Another major interest is functional genomics and systems biology, where we have been concerned with predicting molecular networks using genome sequence data, and the use of large genomic data sets (DNA microarrays, proteomics etc) in gene function and gene network prediction. More recently we have worked extensively with new generation sequencing data in the context of genetic regulatory network prediction.

Current major projects include:
  • Genetic regulation of blood cell differentiation
  • Prognostic and diagnostic prediction for blood cell cancers
  • Metabolic systems biology of the malaria parasite

Bioinformatics: the use of computational methods to advance biological understanding

The group is interested in prediction methods for biological problems based on machine learning and statistical methods. Recently we have worked on predicting protein structure, protein-protein interaction sites, gene function and the functional effects of single nucleotide polymorphisms. Another major interest is functional genomics and systems biology, where we have been concerned with predicting molecular networks (particularly metabolism in parasites for drug target applications) using genome sequence data, and the use of large genomic data sets (DNA microarrays, proteomics etc) in gene function and gene network prediction. More recently we have worked extensively with new generation sequencing data in the context of genetic regulatory network prediction.

An increasing interest is genetic regulatory processes in blood cells, as they relate to the differentiation formation of normal myeloid and lymphoid cells, and in their dysfunction in a variety of blood cell cancers. This work is in collaboration with a number of experimental groups, and emphasises the analysis of large, genome wide data sets generation by new generation sequencing technologies. Related to this are our efforts to build machine learing methods able to use genomic information to classify cancers into prognostic and diagnostic categories to inform treatment. The Figure shows one of our machine learning classifiers used to separate diffuse large B cell lymphomas into two categories (Activated B cell (ABC) and germinal centre B cell (GCB)) with different prognosis. This is presently being used in a clinical trial of a new drug for ABC patients.

Figure 1. Survival curves for patients separated into ABC (red) and GCB (blue) type diffuse large B cell lymphoma by our machine learning based classifier. Two data sets are shown, the HMDS set is from St. James Hospital and uses microarray data from paraffin embedded samples; GSE10846 is a publicly available data set.

Detailed research programme                  Close ▲
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Professor of Bioinformatics, Head of School of Molecular and
MA (Cambridge), D. Phil (Oxford)


Professor (Leeds) 2006-present
Senior Lecturer (Leeds) 2003-2006
Lecturer (Leeds) 1998-2003
Post doctoral research fellow, European Bioinformatics Institute, 1996

Garstang 10.127a
School of Molecular and Cellular Biology
0113 343 3116
d.r.westhead@leeds.ac.uk
http://www.bioinformatics.leeds.ac.uk

Selected Publications

  1. 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.

  2. Sha C, Barrans S, Care MA, Cunningham D, Tooze RM, Jack A, Westhead DR. Transferring genomics to the clinic: Distinguishing Burkitt and diffuse large B cell lymphomas Genome Medicine 7 -, 2015

  3. Ptasinska A, Assi SA, Martinez-Soria N, Imperato MR, Piper J, Cauchy P, Pickin A, James SR, Hoogenkamp M, Williamson D, Wu M, Tenen DG, Ott S, Westhead DR, Cockerill PN, Heidenreich O, Bonifer C Identification of a dynamic core transcriptional network in t(8;21) AML that regulates differentiation block and self-renewal Cell Reports 8 1974-1988, 2014

  4. Care MA, Barrans S, Worrillow L, Jack A, Westhead DR, Tooze RM A microarray platform-independent classification tool for cell of origin class allows comparative analysis of gene expression in diffuse large B-cell lymphoma. PLoS One 8 e55895-, 2013.