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WILF
2005
Springer

Learning Bayesian Classifiers from Gene-Expression MicroArray Data

10 years 6 months ago
Learning Bayesian Classifiers from Gene-Expression MicroArray Data
Computing methods that allow the efficient and accurate processing of experimentally gathered data play a crucial role in biological research. The aim of this paper is to present a supervised learning strategy which combines concepts stemming from coding theory and Bayesian networks for classifying and predicting pathological conditions based on gene expression data collected from micro-arrays. Specifically, we propose the adoption of the Minimum Description Length (MDL) principle as a useful heuristic for ranking and selecting relevant features. Our approach has been successfully applied to the Acute Leukemia dataset and compared with different methods proposed by other researchers.
Andrea Bosin, Nicoletta Dessì, Diego Libera
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where WILF
Authors Andrea Bosin, Nicoletta Dessì, Diego Liberati, Barbara Pes
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