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Dr David C. Wedge
Research Fellow in Informatics / Cancer Metabolomics
Laboratory for Bioanalytical Spectroscopy
School of Chemistry
The University of Manchester
Manchester Interdisciplinary Biocentre
131 Princess Street
Manchester M1 7DN
Dr David Wedge

Personal | Research | Education | Publications | Software | Contact


Dr David C. Wedge

School of Chemistry, The University of Manchester, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester M1 7DN

Tel: +44 (0)161 306 5145



Cancer metabolomics
Metabolomics is the study of small molecules present in, ingested by or excreted from biological cells, tissues, organs or organisms. It complements the areas of genomics, transcriptomics and proteomics and like these areas aims to obtain an understanding of biological systems as a whole. It is therefore often associated with a Systems Biology approach.

In collaboration with the Paterson Institute for Cancer Research and the University of Manchester School of Cancer and Imaging Studies my research aims to gain a better understanding of the causes, progress and possible treatments of cancer through metabolomic profiling and the integration of metabolic profiles with proteomic and genomic profiles.

The data that I analyse are highly multivariate and the relationships between variables are often strongly non-linear. In order to understand these complex data I use a variety of machine learning techniques, including Bayesian Networks, Genetic Programming and Artificial Neural Networks.

Previous research

Previously, I have worked on a number of interesting projects. These include the identification of proteins via the N-terminal enrichment of enzymatically digested proteins prior to mass spectrometric analysis, the use of Genetic Programming for recognising patterns in data obtained from arrays of organic field effect transistors (OFETs), the use of neural networks as a tool for lie detection through gesture analysis at Silent Talker, the prediction of wave-overtopping rates through the use of hybrid artificial neural networks and the development of web-based mapping software at Graticule.

I am also interested in theoretical ideas in Compter Science. In 2006 I develeoped the global-local artificial neural network (GL-ANN). This combined 2 different types of artificial 'neuron' to produce a neural network that is adept at modelling relationships between clustered data. In 2008 I developed the 'genotype-fitness correlation' (GFC). This is a metric that indicates the smoothness of a problem landscape and is a useful tool in predicting optimal population sizes during GP. In 2009 and 2010 I carried out a number of investigations involving the modelling of DNA fitness landscapes using synthetic models such as NK-landscapes.

For further information about any of this research, see my publications.


2005 Manchester Metropolitan University, PhD Computer Science, Wave Overtopping Prediction using Global-Local Artificial Neural Networks. This project aimed to predict the rate of overtopping of sea-walls with a variety of structures and under a variety of environmental conditions. Theoretical advances were made in the field of hybrid artificial neural networks through the invention of a novel architecture and training method for networks containing both sigmoidal and radial basis function (RBF) neurons. This research took place within the Centre for Mathematical Modelling and Flow Analysis under the guidance of Prof. David Ingram. My thesis is available for download here.

2001 University of Huddersfield, MSc Software Development, Distinction with a prize for the best postgraduate student. This included a research project on the introduction of heuristics into constraint checking during AI Planning under the supervision of Prof. Lee McCluskey.

1993 Bretton Hall College, University of Leeds, PGCE Early Years.

1989 Pembroke College, University of Oxford, BA(Hons.), Chemistry 2(i) with Distinction in History and Philosophy of Science



Brown M., Wedge D.C., Goodacre R., Kell D.B., Baker P.N., Kenny L.C., Mamas M.A., Neyses L. and Dunn W.B. Automated Workflows for Accurate Mass-based Putative Metabolite Identification in LC/MS-derived Metabolomic Datasets, Bioinformatics, In Press

Wedge D.C., Krishna R., Blackhurst P., Siepen J.A., Jones A.R. and Hubbard S.J. FDRAnalysis: A tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines, Journal of Proteome Research, In Press, doi: 10.1021/pr101157s

Rowe W., Wedge D.C., Platt M., Kell D.B. and Knowles J.D (2010) Predictive models for population performance on real biological fitness landscapes, Bioinformatics, 26(17), 2145-2152, doi:10.1093/bioinformatics/btq353

Rowe W., Platt M., Wedge D.C., Day P.J.R., Kell D.B. and Knowles J.D. (2010) Convergent evolution to an aptamer observed in small populations on DNA microarrays, Physical Biology, 7(3), doi: 10.1088/1478-3975/7/3/036007

Kettle J., Whitelegg S., Song A.M., Wedge D.C., Kotacka L., Kolarik V., Madec M.-B., Yeates S.G. and Turner M.L. (2010) Fabrication of planar organic nanotransistors using low temperature thermal nanoimprint lithography for chemical sensor applications, Nanotechnology, 21(7), 075301, doi: 10.1088/0957-4484/21/7/075301

Wedge, D.C., Rowe, W., Kell, D.B. and Knowles, J. (2009) In silico Modelling of Directed Evolution: Implications for Experimental Design and Stepwise Evolution, Journal of Theoretical Biology, 257, 131-141, doi:10.1016/j.jtbi.2008.11.005

Wedge D.C., Das A., Dost R., Kettle J., Madec M.-B., Morrison J.J., Grell M., Kell D.B., Richardson T.H., Yeates S. and Turner M.L. (2009) Real-time vapour sensing using an OFET-based electronic nose and genetic programming, Sensors and Actuators B, 143(1), 365-372, doi:10.1016/j.snb.2009.09.030

Das, A., Dost, R., Richardson, T.H., Grell, M., Wedge, D.C., Morrison, J.J. and Turner, M.L. (2009), Low cost, portable, fast multiparameter data acquisition system for organic transistor odour sensors, Sensors and Actuators B, 137(2), 586-591, doi: 10.1016/j.snb.2009.01.006

Knight, C.G., Platt, M., Rowe, W., Wedge, D.C., Khan, F., Day, P.J.R., McShea, A., Knowles, J. and Kell, D.B. (2009) Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape, Nucleic Acids Research, 37(1), e6, doi: 10.1093/nar/gkn899

Platt, M., Rowe, W., Wedge, D.C., Kell, D.B., Knowles, J. and Day, P.J.R. (2009) Aptamer evolution for array-based diagnostics, Analytical Biochemistry, 390, 203-205

Harding A.P., Wedge D.C. and Popelier, P.L.A. (2009) pKa Prediction from “Quantum Chemical Topology” Descriptors, Journal of Chemical Information and Modeling, 49(8), 1914-1924

Rowe, W., Platt, M., Wedge D.C., Day, P.J., Kell, D.B. and Knowles, J. (2009) Analysis of a Complete DNA-Protein Affinity Landscape, Journal of the Royal Society Interface, doi: 10.1098/rsif.2009.0193

Wedge, D.C., Ingram, D., McLean, D., Mingham, C. and Bandar, Z. (2006) On Global-Local Artificial Neural Networks for Function Approximation, IEEE Transactions on Neural Networks, 17(4), 942-952

Wedge, D.C., Ingram, D., McLean, D., Mingham, C. and Bandar, Z. (2005) Neural Network Architectures and Wave Overtopping, Maritime Engineering, 158(MA3), 123-133

Conference Proceedings:

Wedge, D.C. and Kell, D.B. (2008) Rapid Prediction of Optimum Population Size in Genetic Programming Using a Novel Genotype - Fitness Correlation, GECCO 2008: Proceedings of the 10th annual conference on Genetic and Evolutionary Computation, M. Keijzer et al (eds.) 2219-2225

Wedge, D.C., Hubbard, S., Gaskell, S.J., Kell, D.B., Lau, K.W. and Eyers, C. (2007) Peptide Detectability following ESI Mass Spectrometry: Prediction using Genetic Programming, GECCO 2007: Proceedings of the 9th annual conference on Genetic and Evolutionary Computation, H. Lipson et al (eds.) 1315-1322

Wedge, D.C., Ingram, D., McLean, D., Mingham, C. and Bandar, Z. (2005) A Global-Local Artificial Neural Network with Application to Wave Overtopping Prediction, Proceedings of the International Conference on Artificial Neural Networks (ICANN), W. Duch et al (eds.) 109-114


'GeneticWedge', the computer code used to run the Genetic Programming algorithm, is written in the Java progamming language. Source code and class files are available for download here. A jar file containing the Java class files is available here. Documentation in standard javadoc format is available here. Steve O'Hagan has created a GUI 'front-end', which allows access to the basic functionality of GeneticWedge and is available here.

I was involved in creating the FDRAnalysis website, which combines the outputs of different search engines applied to MS/MS protein analysis using false discovery rate (FDR) scores. A desktop version of the software is available here.

If you use any of this software in your research please credit me. Also any feedback (positive or negative) would be welcome.

Contact Details


Address: School of Chemistry
The University of Manchester
Manchester Interdisciplinary Biocentre
131 Princess Street
Manchester M1 7DN
United Kingdom

Telephone: +44 (0)161 306 5145

Personal | Research | Education | Publications | Software | Contact

Last update: 9 February 2011


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