Dr Julie Wilson
The York Centre for Complex Systems Analysis (YCCSA)
Chemometrics, biostatistics and image analysis
My research interests lie in the application of mathematical modelling and statistical methods to biological and chemical problems. In collaboration with the Analytical and Atmospheric Chemistry Groups, we are developing chemometric techniques for the handling and processing of the very large data sets obtained by -omic techniques. Current projects include the integration of data from different technologies (transcriptomic, proteomic and metabolomic data) and data fusion methods to combine data sets from multiple analytical techniques (e.g. LC-MS and NMR). Through a well-established collaboration with the Food and Environment Research Agency (FERA), we have been developing methods for discriminative analysis in the search for biomarkers for disease, the detection of contaminants, and to monitor genetic modification.
In contrast to the mega-variate data sets often obtained by chemical and biochemical analysis, the classification of images usually involves the extraction of relatively few relevant features. The software ALICE (AnaLysis of Images from Crystallisation Experiments), developed in collaboration with structural biology laboratories throughout Europe, aims to identify the results of crystallization experiments to provide information for subsequent trials in an automated procedure. Image analysis techniques are also being applied in bioarchaeology (with BioArCh), where starch granule morphology is used to make inferences about ancient human diet. Other projects with BioArCh include ODE modeling of peptide degradation and the de-convolution of isotope distributions to determine the extent of glutamine deamidation and species identification using peptide mass fingerprinting.
- Peak fitting in 2D 1H-13C HSQC NMR spectra for metabolomics studies.
J S McKenzie, A J Charlton, J A Donarski, A D MacNicoll and J C Wilson, Metabolomics. J Theor Biol, 2010, 60,131-160.
- Automated classification of starch granules using supervised pattern recognition of morphological properties.
J Wilson, K Hardy, R Allen, L Copeland, R Wrangham and M Collins, J. Arch.Sci., 2010, 37, 594–604.
- ZooMS, the collagen barcode and fingerprints.
M Collins, M Buckley, J Thomas-Oates, J C Wilson and N van Doorn, Spectroscopy Europe, 2010, 22, 11-13.
- Metabolomic appliations of HILIC-LC-MS.
S Cubbon, C Antonio, J Wilson and J Thomas-Oates, Mass Spectrom. Rev., 2010, 29, 671-84.
- Species identification by analysis of bone collagen using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry.
M Buckley, M Collins, J Thomas-Oates and J C Wilson, Rapid Commun. Mass Spectrom., 2009, 23, 3843–3854.
- Local modelling in classification.
G Szepannek, J Schiffner, J Wilson and C Weihs, in "Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects", ed. P. Perner. lnai 5077, Springer Verlag, Heidleberg, 2008.
- Improved classification of crystallization images using data fusion and multiple classifiers.
S Buchala. and J Wilson, Acta Cryst., 2008, D64, 823-833.
- Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques.
D Watts, K Cowtan and J Wilson, J.Appl. Cryst., 2008, 41, 8-17.
- Removal of t1 Noise from 2D 1H-13C HSQC NMR Spectra by Correlated Trace Denoising.
S Poulding, A J Charlton, J Donarski and J C Wilson, J. Mag. Res., 2007, 189, 190-199.
- Hydrophilic Interaction Chromatography for Mass Spectrometric Metabonomic Studies of Urine.
S Cubbon, T Bradbury, J Wilson and J Thomas-Oates, Anal. Chem., 2007, 79, 8911-8918.
- Classification of protein crystallisation images using Fourier descriptors.
C G Walker, J Foadi and J Wilson, J Appl Cryst, 2007, 40, 418-426.
- Adaptive Binning: An Improved Binning Method forr metabolomics Data Using the Undecimated Wavelet Transform.
R A Davis, A J Charlton, J Godward, S A Jones, M Harrison and J C Wilson, Chemom Intell Lab Sys, 2007, 85, 144-154.
- Data processing in metabolomics fingerprinting by CE-UV: application to urine samples from autistic children.
A C Soria, D G Goodall, B Wright and J C Wilson, Electrophoresis, 2007, 28, 950-964.
- A novel feature selection method for genetic programming using 1H NMR data.
R Davis, A Charlton, A., S Oehlschlager and J Wilson, Chemom Intell Lab Syst, 2006, 81, 50-59.