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IDA
2005
Springer

Bayesian Networks Learning for Gene Expression Datasets

11 years 10 months ago
Bayesian Networks Learning for Gene Expression Datasets
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.
Giacomo Gamberoni, Evelina Lamma, Fabrizio Riguzzi
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where IDA
Authors Giacomo Gamberoni, Evelina Lamma, Fabrizio Riguzzi, Sergio Storari, Stefano Volinia
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