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.

• Type: Conference
• Year: 2005
• Where: WILF
• Authors: Andrea Bosin, Nicoletta Dessì, Diego Liberati, Barbara Pes
• Keywords: Acute Leukemia Dataset, Artificial Intelligence, Gene Expression Data, Supervised Learning Strategy, WILF 2005