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. © Springer-Verlag Berlin Heidelberg 2006.

Learning Bayesian classifiers From gene-expression MicroArray data

Liberati Diego;
2006

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. © Springer-Verlag Berlin Heidelberg 2006.
2006
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
Bayesian Classifiers
Feature Se-lection
Gene-Expression Data Analysis
MDL
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/362414
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