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
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
Hashem Shihab, Mark Rogers, Colin Campbell and Tom Gaunt.
HIPred: an integrative approach to predicting haploinsufficient genes.Bioinformatics https://doi.org/10.1093/bioinformatics/btx028 (2017).
Hashem A. Shihab, Mark F. Rogers, Michael Ferlaino, Colin Campbell and Tom R. Gaunt.
GTB - an online genome tolerance browser.
BMC Bioinformatics 2017, 18:20, DOI: 10.1186/s12859-016-1436-4. The
Genome Tolerance Browser enables visualisation
of predicted tolerance of genomic regions to mutational variation.
It includes 13 genome-wide prediction algorithms and conservation scores,
12 non-synonymous prediction algorithms and four cancer-specific algorithms.
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 (Nature) 6, article number: 35671 (2016)
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)
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
. 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:
The MRC-supported Bristol Centre for Systems Biomedicine (BCSBmed)
with 5 studentships available per annum. This initiative supports PhD projects in bioinformatics
and cancer informatics in addition to other research topics.
Departmental Training Awards (DTAs).
In addition there are further Bristol university scholarships for
both UK/EU candidates
(non-UK and non-EU) candidates.
Occasionally we also have other PhD studentships which are open to nationals
of any country and are linked to a specific project.
Please email Dr. Campbell directly (C.Campbell(at)bris.ac.uk) for further information.
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.
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
, Professor at the University of Bristol, Simon Rogers
of Glasgow) and Stephen Coombes
, Professor at the University of Nottingham.
Various talks on kernel-based learning.
Past Workshops organised:
Cancer Bioinformatics Workshop, Cambridge, 2-4 September, 2010
Several NIPS Workshops on
machine learning and