Personal webpage: Colin Campbell
Room 2.39, Merchant Venturer's Building,
University of Bristol,
Bristol BS8 1UB,
Tel: +44 (0)117-33-15620
Fax: +44 (0)117-954-5208 (attn: Colin Campbell (EMAT))
I gained a First Class Honours degree in Physics from Imperial College, London and a Doctorate from the Department of Mathematics, King's College, London, under the supervision of Prof. Peter West FRS. I am currently a Reader in the Merchant Venturer's School of Engineering, University of Bristol. For research purposes our group is a component part of the Intelligent Systems Laboratory at the University of Bristol, of which I'm currently Head. For teaching purposes I am associated with the Department of Engineering Mathematics. My research interests are machine learning, including probabilistic graphical models and kernel-based methods, algorithm design and the applications of machine learning techniques in bioinformatics, particularly medical bioinformatics. Our research is currently funded by the generous support of the the EPSRC and the Medical Research Council.
Recent Journal Papers
- Tom G Richardson, Nicholas J Timpson, Colin Campbell and Tom R Gaunt. A pathway-centric approach to rare variant association analysis. European Journal of Human Genetics (www.nature.com/ejhg), (2016), 1-7, doi:10.1038/ejhg.2016.113.
- Richardson T.G., Campbell C., Timpson N.J. and Gaunt T.R. Incorporating Non- Coding Annotations into Rare Variant Analysis. PLoS ONE 11(4) (2016): e0154181.
- Richardson T.G., Shihab H.A., Rivas M.A., McCarthy M.I., Campbell C., Timpson N.J. and Gaunt T.R. A Protein Domain and Family Based Approach to Rare Variant Association Analysis. PLoS ONE 11(4) (2016): e0153803.
- Richardson T.G. et al. Collapsed Methylation Quantitative Trait Loci analysis for Low Frequency and Rare variants. Human Molecular Genetics (2016) doi: 10.1093/hmg/ddw283.
- Lulu Jiang, Charles C. T. Hindmarch, Mark Rogers, Colin Campbell, Christy Waterfall, Jane Coghill, Peter W. Mathieson and Gavin I. Welsh. RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways. Scientific Reports 6, article number: 35671 (2016) doi:10.1038/srep35671.
- Carlos Fernandez-Lozano, Jose A. Seoane, Marcos Gestal, Tom R. Gaunt, Julain Dorado, Alejandro Pazos and Colin Campbell. Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Scientific Reports (Nature), 6, Article number 19256 (2016).
- C. Rivers, H. Scott, M. Rogers, Y. Lee, G. Toye, J. Idris, J. Gaunt, C. Hales, T. Curk, C. Campbell, J. Ule, M. Norman, J. B. Uney. iCLIP identifies novel neuronal roles for SAFB1 in regulating RNA processing and neuronal function. BMC Biology 13:111 (2015)
- Su-yi Loh, Thomas Jashans-Price, Michael Greenwood, Mingkwan Greenwood, See-Ziau Hoe, Agnieszka Konopacka, Colin Campbell, David Murphy and Charles C.T. Hindmarch. Unsupervised gene network reconstruction of the plastic suproptic nucleus transcriptome predicts regulatory interactions. Scientific Reports (Nature)
- Hashem Shihab, Mark Rogers, Julian Gough, Matthew Mort, David Cooper, Ian Day, Tom Gaunt and Colin Campbell. An Integrative Approach to Predicting the Functional Effects of Non-Coding and Coding Sequence Variation Bioinformatics 31(10): 1536-1543 (2015). Supplementary information and website for the coding/non-coding predictor. This method uses integrative machine learning methods to predict if single nucleotide variants in the human genome are likely functional in disease. The predictor outputs a confidence label associated with the prediction and it gives predictions for sequence variants in both the coding and non-coding regions of the human genome. There is further information about this approach and various extensions of this project in the Available software submenu on the left.
Book on kernel-based learning methods:Learning with Support Vector Machines by Colin Campbell and Yiming Ying (Morgan and Claypool, 2011). This is a concise introduction to Support Vector Machines and kernel-based learning. It is available as an ebook from Morgan and Claypool or as hardcopy from Amazon or Waterstones. Introductory Lectures on Support Vector Machines, from the 6th International Summer School on Pattern Recognition, provide a shortened summary of some of the content of this book.
PhD funding for projects in our areas of interest is available from a number of sources:
Postdoctoral Opportunities:For very well qualified candidates we support applications for Fellowship awards from a variety of research sponsors.
Previous Group Members:Michael Ferlaino has moved to the Data Science Institute, University of Oxford. Yiming Ying is now Professor in the Department of Mathematics and Statistics, State University of New York at Albany. Kaizhu Huang is now Associate Professor at the National Laboratory of Pattern Recognition, The Chinese Academy of Sciences, Beijing, China. UK-based academic staff who are alumni of the group include Nello Cristianini, Professor at the University of Bristol, Simon Rogers (University of Glasgow) and Stephen Coombes, Professor at the University of Nottingham.
Past Workshops organised: