Title: Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
Authors: Gevaert, Olivier ×
De Smet, Frank
Timmerman, Dirk
Moreau, Yves
De Moor, Bart #
Issue Date: Jul-2006
Publisher: Oxford University Press
Series Title: Bioinformatics vol:22 issue:14 pages:E184-E190
Conference: ISMB 2006 location:Fortaleza, Brazil date:Jul. 2006
Abstract: 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.
ISSN: 1367-4803
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Electrical Engineering - miscellaneous
Basic Research in Gynaecology Section (-)
Environment and Health - miscellaneous
× corresponding author
# (joint) last author

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