Skip Navigation


Bioinformatics Advance Access originally published online on November 14, 2008
Bioinformatics 2009 25(1):112-118; doi:10.1093/bioinformatics/btn586
This Article
Right arrow Full Text
Right arrow Full Text (Print PDF)
Right arrow All Versions of this Article:
25/1/112    most recent
btn586v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Cavill, R.
Right arrow Articles by Ebbels, T. M. D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Cavill, R.
Right arrow Articles by Ebbels, T. M. D.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Genetic algorithms for simultaneous variable and sample selection in metabonomics

Rachel Cavill *, Hector C. Keun , Elaine Holmes , John C. Lindon , Jeremy K. Nicholson and Timothy M. D. Ebbels *

Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: Metabolic profiles derived from high resolution 1H-NMR data are complex, therefore statistical and machine learning approaches are vital for extracting useful information and biological insights. Focused modelling on targeted subsets of metabolites and samples can improve the predictive ability of models, and techniques such as genetic algorithms (GAs) have a proven utility in feature selection problems. The Consortium for Metabonomic Toxicology (COMET) obtained temporal NMR spectra of urine from rats treated with model toxins and stressors. Here, we develop a GA approach which simultaneously selects sets of samples and spectral regions from the COMET database to build robust, predictive classifiers of liver and kidney toxicity.

Results: The results indicate that using simultaneous sample and variable selection improved performance by over 9% compared with either method alone. Simultaneous selection also halved computation time. Successful classifiers repeatedly selected particular variables indicating that this approach can aid defining biomarkers of toxicity. Novel visualizations of the results from multiple computations were developed to aid the interpretability of which samples and variables were frequently selected. This method provides an efficient way to determine the most discriminatory variables and samples for any post-genomic dataset.

Availability: GA code available from http://www1.imperial.ac.uk/medicine/people/r.cavill/

Contact: r.cavill{at}imperial.ac.uk; t.ebbels{at}imperial.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jonathan Wren

Received on August 4, 2008; revised on October 17, 2008; accepted on November 9, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.