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With this case study we turn to the field of cancer classification by means of microarray analysis. One of the challenges in microarray analysis is the sheer number of genes, which could potentially be predictors in a classification model. At the same time, the number of observations tends to be small.
Our objective is to show that our modeling approach with Bayesian networks (as the framework), BayesiaLab (as the software tool) and the Augmented Markov Blanket (as the algorithm) can quickly and effectively generate models of equal or better classification performance compared to models documented in literature, while only requiring a minimum of specification effort from the research analyst.
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