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Bayesian Networks Learning for Gene Expression Datasets
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Bayesian Networks Learning for Gene Expression Datasets Giacomo Gamberoni1  , Evelina Lamma1 , Fabrizio Riguzzi1 , Sergio Storari1 and Stefano Volinia2
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ENDIF-Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy |
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Dipartimento di Biologia, Università di Ferrara, Ferrara, Italy |
Abstract
DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network as an appropriate tool for develop similar models. In this paper, we exploit the contribute of two Bayesian network learning algorithms to generate genetic networks from microarray datasets of experiments performed on Acute Myeloid Leukemia (AML).
In the results, we present an analysis protocol used to synthesize knowledge about the most interesting gene interactions and compare the networks learned by the two algorithms. We also evaluated relations found in these models with the ones found by biological studies performed on AML.
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