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Bioinformatics 2006 22(14):e184-e190; doi:10.1093/bioinformatics/btl230
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks

Olivier Gevaert 1,*, Frank De Smet 1,2, Dirk Timmerman 3, Yves Moreau 1 and Bart De Moor 1

1 Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven Kasteelpark Arenberg 10, 3001 Leuven, Belgium
2 Medical Direction, National Alliance of Christian Mutualities Haachtsesteenweg 579, 1031 Brussel, Belgium
3 Department of Obstetrics and Gynecology, University Hospital Gasthuisberg, Katholieke Universiteit Leuven Herestraat 49, 3000 Leuven, Belgium

*To whom correspondence should be addressed.

Motivation: Clinical data, such as patient history, laboratory analysis, ultrasound parameters—which are the basis of day-to-day clinical decision support—are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable.

Results: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices.

Contact: olivier.gevaert{at}esat.kuleuven.be


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