|Titolo:||Learning Bayesian Classifiers from Gene-Expression MicroArray Data|
|Data di pubblicazione:||2006|
|Citazione:||Learning Bayesian Classifiers from Gene-Expression MicroArray Data / BOSIN A; DESSI' N; LIBERATI D; PES B. - 3849(2006), pp. 297-304. ((Intervento presentato al convegno WILF 2005: 6th International Workshop onFuzzy Logic and Applications, tenutosi a Crema, Italy nel September 15-17, 2005.|
|Abstract:||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.|
|Tipologia:||4.1 Contributo in Atti di convegno|
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